In [1]:
# Some imports to get us started
import warnings
warnings.simplefilter('ignore')
# Utilities
import os
import sys
import urllib.request
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
random_state = 100 # Ensure reproducible results
# DoWhy
import pygraphviz
from IPython.display import Image, display
import dowhy
from dowhy import CausalModel
import econml
# Generic ML imports
from scipy.stats import uniform, truncnorm, randint
from sklearn.utils.fixes import loguniform
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVC
from sklearn.naive_bayes import BernoulliNB, CategoricalNB
from sklearn.linear_model import LassoCV, LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.dummy import DummyClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss, recall_score, f1_score, roc_auc_score, confusion_matrix, plot_roc_curve
from sklearn.model_selection import cross_validate, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
# Import custom dowhy helper functions module
cwd = Path().resolve()
PARENT_DIR = os.path.dirname(cwd)
SCRIPT_DIR = os.path.join(PARENT_DIR, 'helpers')
sys.path.append(SCRIPT_DIR)
import meta_model_helpers as mmh
In [2]:
# I/O Stuff
DATA_FILENAME = "csdh_burr.csv"
DATA_FILEPATH = "/Users/callum/Uni/GitHubRepos/surviving-the-icu/datasets/drain_data/" + DATA_FILENAME
csdh = pd.read_csv(DATA_FILEPATH)
In [3]:
# DOCTOR DAG features
doc_features = ['age', 'stroke', 'ihd', 'metalvalve', 'antiplatelet', 'warfarin', 'hospital',
'thickness_sum', 'density', 'optype', 'membranes', 'burrhole_num', 'bedrest',
'drain', 'recurrence']
In [4]:
# Categorical type conversion
categorical_features = ['stroke', 'antiplatelet', 'ihd', 'metalvalve', 'membranes', 'optype', 'recurrence',
'drain', 'hospital', 'bedrest', 'warfarin', 'density', 'membranes', 'burrhole_num',
'bedrest']
for feature in categorical_features:
col = pd.Categorical(csdh[feature])
csdh[feature] = col.codes
In [5]:
csdh_doc = csdh[doc_features]
In [6]:
doc_model = CausalModel(data=csdh,
treatment='drain',
outcome='recurrence',
graph='../causal_graphs/doctor_dag.dot'.replace("\n", " "))
doc_model.view_model()
display(Image(filename="causal_model.png"))
Define set of classifiers to investigate¶
In [7]:
names = ['Dummy', 'LR', 'Linear SVM', 'RBF SVM', 'GB', 'RF', 'XGB']
In [8]:
# Naive Bayes exluded due to mixture of variable types
classifiers = [
DummyClassifier(strategy='most_frequent'),
LogisticRegression(max_iter=1000),
SVC(kernel="linear", probability=True, random_state=random_state),
SVC(kernel='rbf', probability=True, random_state=random_state),
GradientBoostingClassifier(random_state=random_state),
RandomForestClassifier(random_state=random_state),
XGBClassifier(random_state=random_state),
]
In [9]:
# define cross-validation structure
cv_5 = StratifiedKFold(n_splits=5, shuffle=True, random_state=random_state)
cv_10 = StratifiedKFold(n_splits=10, shuffle=True, random_state=random_state)
In [10]:
# define classifiers and hyperparameters to search over
rf = RandomForestClassifier()
params_rf = {
# randomly sample numbers from 10 to 200 estimators
'rf__n_estimators':randint(10, 200),
### DONT TUNE THESE DUE TO DATASET SIZE ###
# minimum number of samples required to split an internal node
#'rf__min_samples_split':randint(1, 12),
# minimum number of samples required to split a leaf
#'rf__min_samples_leaf':randint(1, 50),
# The maximum depth of the individual regression estimators.
'rf__max_depth':randint(2, 15),
# The number of features to consider when looking for the best split
'rf__max_features':['sqrt', 'log2', None],
# random seed
'rf__random_state':[random_state],
# Whether bootstrap samples are used when building trees
'rf__bootstrap':[True, False]
}
gb = GradientBoostingClassifier()
params_gb = {
# randomly sample numbers from 10 to 200 estimators
'gb__n_estimators':randint(10, 200),
# fraction of samples to be used for fitting individual base learners
'gb__subsample':[0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 1],
# learning rate
'gb__learning_rate':[0.001, 0.003, 0.01, 0.03, 0.07, 0.1, 0.3, 0.7, 1.0],
### DONT TUNE THESE DUE TO DATASET SIZE ###
# minimum number of samples required to split an internal node
#'gb__min_samples_split':randint(1, 12),
# minimum number of samples required to split a leaf
#'gb__min_samples_leaf':randint(1, 50),
# The maximum depth of the individual regression estimators
'gb__max_depth':randint(2, 15),
# The number of features to consider when looking for the best split
'gb__max_features':['sqrt', 'log2', None],
# random seed
'gb__random_state':[random_state]
}
xgb = XGBClassifier()
params_xgb = {
# randomly sample numbers from 10 to 200 estimators
'xgb__n_estimators':randint(10, 200),
# fraction of samples to be used for fitting individual base learners
'xgb__subsample':[0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 1],
# learning rate
'xgb__learning_rate':[0.001, 0.003, 0.01, 0.03, 0.07, 0.1, 0.3, 0.7, 1],
# min_split_loss
'xgb__gamma':[0.001, 0.003, 0.01, 0.03, 0.07, 0.1, 0.3],
### DONT TUNE THESE DUE TO DATASET SIZE ###
# minimum number of samples required to split an internal node
#'gb__min_samples_split':randint(1, 12),
# minimum number of samples required to split a leaf
#'gb__min_samples_leaf':randint(1, 50),
# The maximum depth of the individual regression estimators
'xgb__max_depth':randint(2, 15),
# analagous to max_features in rf and gb
'xgb__colsample_bytree':[0.6, 0.7, 0.8, 0.9, 1],
# random seed
'xgb__random_state':[random_state],
}
In [11]:
# define search spaces for random search tuning
search_space = [('rf', rf, params_rf), ('gb', gb, params_gb), ('xgb', xgb, params_xgb)]
Predicting $T$ (drain) from controls $X, W$¶
In [12]:
y_drain_full = csdh_doc['drain']
X_drain_full = csdh_doc.drop(['drain', 'recurrence'], axis=1)
In [13]:
# Split into validation set and rest
X_drain_rest, X_drain_test, y_drain_rest, y_drain_test = train_test_split(X_drain_full, y_drain_full,
test_size=0.20,
random_state=random_state,
stratify=y_drain_full)
# Split rest into train and test set
X_drain_train, X_drain_val, y_drain_train, y_drain_val = train_test_split(X_drain_rest, y_drain_rest,
test_size=0.20,
random_state=random_state,
stratify=y_drain_rest)
In [14]:
drain_training_scores, drain_val_scores = mmh.train_and_validate_classifiers(X_drain_train,
y_drain_train,
X_drain_val,
y_drain_val,
names,
classifiers)
[13:50:30] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
In [15]:
mmh.print_metrics_table(drain_training_scores, drain_val_scores, names)
Classification performance on validation set:
----------------Validation----------------- -----------------Training------------------
Method Acc↑ AUROC↑ Recall↑ F1↑ LL↓ Acc↑ AUROC↑ Recall↑ F1↑ LL↓
----------------------------------------------------------------------------------------------------
Dummy 0.842 0.500 1.000 0.914 5.454 0.839 0.500 1.000 0.913 5.545
LR 0.874 0.600 1.000 0.930 4.363 0.845 0.530 0.994 0.915 5.363
Linear SVM 0.842 0.500 1.000 0.914 5.454 0.839 0.500 1.000 0.913 5.545
RBF SVM 0.842 0.500 1.000 0.914 5.454 0.839 0.500 1.000 0.913 5.545
GB 0.874 0.681 0.963 0.928 4.363 0.971 0.910 1.000 0.983 1.000
RF 0.853 0.587 0.975 0.918 5.090 1.000 1.000 1.000 1.000 0.000
XGB 0.821 0.623 0.912 0.896 6.181 1.000 1.000 1.000 1.000 0.000
model_t K-Fold cross validation for hyperparameter tuning and model selection¶
In [16]:
# do the search
_,_, best_estimator_drain = mmh.randomized_search_cv(X_drain_rest, y_drain_rest,
search_space,
cv=cv_5,
refit=True,
score='roc_auc',
n_iter=5000,
verbose=True)
cv strategy StratifiedKFold(n_splits=5, random_state=100, shuffle=True)
----------------------------------------
Trial 0
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='log2',
n_estimators=109, random_state=100,
subsample=0.75))])
cv score: [0.61333333 0.61583333 0.6625 0.73916667 0.76107595]
----------------------------------------
Trial 1
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=137, random_state=100))])
cv score: [0.69166667 0.6275 0.735 0.79583333 0.82515823]
----------------------------------------
Trial 2
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.65333333 0.78 0.8 0.87816456]
----------------------------------------
Trial 3
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=68,
random_state=100))])
cv score: [0.63 0.59166667 0.73583333 0.75375 0.79588608]
----------------------------------------
Trial 4
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=14, random_state=100))])
cv score: [0.68083333 0.65333333 0.67083333 0.76291667 0.84651899]
----------------------------------------
Trial 5
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=52, random_state=100,
subsample=0.95))])
cv score: [0.68416667 0.63166667 0.71083333 0.77416667 0.74762658]
----------------------------------------
Trial 6
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69 0.675 0.7925 0.81166667 0.90110759]
----------------------------------------
Trial 7
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=198,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65166667 0.675 0.78416667 0.76916667 0.875 ]
----------------------------------------
Trial 8
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.8))])
cv score: [0.5925 0.64583333 0.68333333 0.71833333 0.56724684]
----------------------------------------
Trial 9
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=113,
random_state=100))])
cv score: [0.68416667 0.59416667 0.6825 0.80583333 0.83306962]
----------------------------------------
Trial 10
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.52333333 0.72458333 0.73708333 0.82083333 0.77294304]
----------------------------------------
Trial 11
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=11, random_state=100,
subsample=0.75))])
cv score: [0.635 0.70458333 0.76291667 0.785 0.78322785]
----------------------------------------
Trial 12
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=147,
random_state=100))])
cv score: [0.69083333 0.63083333 0.69916667 0.805 0.82753165]
----------------------------------------
Trial 13
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
n_estimators=155, random_state=100,
subsample=0.75))])
cv score: [0.68666667 0.665 0.74333333 0.74333333 0.77610759]
----------------------------------------
Trial 14
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2',
random_state=100))])
cv score: [0.67916667 0.60166667 0.72083333 0.81666667 0.82199367]
----------------------------------------
Trial 15
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
n_estimators=65, random_state=100,
subsample=0.6))])
cv score: [0.735 0.67083333 0.785 0.86 0.85205696]
----------------------------------------
Trial 16
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=177,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.6625 0.78876582]
----------------------------------------
Trial 17
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='sqrt',
n_estimators=65, random_state=100,
subsample=0.8))])
cv score: [0.53333333 0.58333333 0.65083333 0.61 0.62974684]
----------------------------------------
Trial 18
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70666667 0.65916667 0.76166667 0.78083333 0.87341772]
----------------------------------------
Trial 19
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=28, random_state=100,
subsample=0.85))])
cv score: [0.74666667 0.6325 0.7875 0.795 0.84414557]
----------------------------------------
Trial 20
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=50, random_state=100))])
cv score: [0.655 0.68083333 0.72 0.78583333 0.83148734]
----------------------------------------
Trial 21
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=86, random_state=100,
subsample=0.75))])
cv score: [0.7425 0.62166667 0.7475 0.82166667 0.83227848]
----------------------------------------
Trial 22
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=96, random_state=100,
subsample=0.8))])
cv score: [0.60583333 0.5375 0.62166667 0.71583333 0.67484177]
----------------------------------------
Trial 23
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=174, random_state=100,
subsample=0.6))])
cv score: [0.71166667 0.58583333 0.68083333 0.77166667 0.83148734]
----------------------------------------
Trial 24
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=135,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67791667 0.73916667 0.79166667 0.83541667 0.87262658]
----------------------------------------
Trial 25
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.55916667 0.625 0.76583333 0.73833333 0.82120253]
----------------------------------------
Trial 26
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=135,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6675 0.6575 0.7875 0.78666667 0.8164557 ]
----------------------------------------
Trial 27
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='sqrt',
n_estimators=69, random_state=100,
subsample=0.7))])
cv score: [0.61583333 0.68125 0.67625 0.78083333 0.81091772]
----------------------------------------
Trial 28
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
n_estimators=136,
random_state=100))])
cv score: [0.70083333 0.53916667 0.79708333 0.69416667 0.61036392]
----------------------------------------
Trial 29
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=126,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65166667 0.62833333 0.76083333 0.7325 0.85047468]
----------------------------------------
Trial 30
----------------------------------------
Parameters {'xgb__n_estimators': 160, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=160,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.65083333 0.79 0.77916667 0.87262658]
----------------------------------------
Trial 31
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.7275 0.74 0.87 0.84493671]
----------------------------------------
Trial 32
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.595 0.66833333 0.7075 0.755 0.78560127]
----------------------------------------
Trial 33
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=104, random_state=100,
subsample=0.75))])
cv score: [0.605 0.62333333 0.68833333 0.7825 0.74367089]
----------------------------------------
Trial 34
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7925 0.71916667 0.80083333 0.845 0.89240506]
----------------------------------------
Trial 35
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=160, random_state=100))])
cv score: [0.67583333 0.6575 0.71916667 0.805 0.85601266]
----------------------------------------
Trial 36
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.7075 0.74833333 0.7975 0.88686709]
----------------------------------------
Trial 37
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=135, random_state=100))])
cv score: [0.67666667 0.64666667 0.7275 0.81583333 0.84256329]
----------------------------------------
Trial 38
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69666667 0.695 0.79833333 0.77083333 0.8789557 ]
----------------------------------------
Trial 39
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.6))])
cv score: [0.6775 0.6925 0.65416667 0.725 0.73496835]
----------------------------------------
Trial 40
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=27, random_state=100,
subsample=0.85))])
cv score: [0.645 0.5625 0.6925 0.80583333 0.81091772]
----------------------------------------
Trial 41
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=58,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78833333 0.73208333 0.7925 0.855 0.88765823]
----------------------------------------
Trial 42
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75333333 0.72833333 0.79666667 0.82833333 0.87262658]
----------------------------------------
Trial 43
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=137, random_state=100))])
cv score: [0.57166667 0.72 0.69416667 0.8025 0.80221519]
----------------------------------------
Trial 44
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=76,
random_state=100))])
cv score: [0.68458333 0.49375 0.8075 0.6475 0.75316456]
----------------------------------------
Trial 45
----------------------------------------
Parameters {'xgb__n_estimators': 99, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=99,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7525 0.75041667 0.78166667 0.85375 0.85522152]
----------------------------------------
Trial 46
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='sqrt',
n_estimators=108,
random_state=100))])
cv score: [0.62916667 0.59916667 0.67083333 0.70666667 0.66218354]
----------------------------------------
Trial 47
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73083333 0.69416667 0.77833333 0.8075 0.89556962]
----------------------------------------
Trial 48
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='sqrt',
n_estimators=120, random_state=100,
subsample=0.7))])
cv score: [0.69416667 0.63833333 0.74666667 0.79 0.81803797]
----------------------------------------
Trial 49
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=75,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78333333 0.72916667 0.80666667 0.83166667 0.83386076]
----------------------------------------
Trial 50
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71916667 0.72416667 0.76375 0.865 0.85838608]
----------------------------------------
Trial 51
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=36,
random_state=100))])
cv score: [0.7175 0.65541667 0.72916667 0.78416667 0.83860759]
----------------------------------------
Trial 52
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=155, random_state=100))])
cv score: [0.70208333 0.61083333 0.73083333 0.81875 0.82911392]
----------------------------------------
Trial 53
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=126, random_state=100,
subsample=0.85))])
cv score: [0.78083333 0.68208333 0.72791667 0.8575 0.85522152]
----------------------------------------
Trial 54
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=135,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.705 0.665 0.78666667 0.805 0.8789557 ]
----------------------------------------
Trial 55
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=41, random_state=100))])
cv score: [0.72416667 0.67125 0.77666667 0.83625 0.84691456]
----------------------------------------
Trial 56
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.775 0.74916667 0.7975 0.8525 0.86155063]
----------------------------------------
Trial 57
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
random_state=100,
subsample=0.95))])
cv score: [0.68333333 0.5625 0.71083333 0.78833333 0.74762658]
----------------------------------------
Trial 58
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
n_estimators=81, random_state=100,
subsample=0.8))])
cv score: [0.6975 0.635 0.74916667 0.74333333 0.75791139]
----------------------------------------
Trial 59
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=72,
random_state=100))])
cv score: [0.69166667 0.62416667 0.68583333 0.77833333 0.82199367]
----------------------------------------
Trial 60
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=48, random_state=100))])
cv score: [0.61333333 0.67916667 0.70791667 0.80583333 0.84177215]
----------------------------------------
Trial 61
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67833333 0.72333333 0.79916667 0.81916667 0.87420886]
----------------------------------------
Trial 62
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=34,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78 0.71791667 0.76833333 0.83333333 0.90268987]
----------------------------------------
Trial 63
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=182,
random_state=100))])
cv score: [0.62583333 0.67 0.7 0.8175 0.82832278]
----------------------------------------
Trial 64
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=110, random_state=100))])
cv score: [0.61083333 0.6975 0.70416667 0.79833333 0.8306962 ]
----------------------------------------
Trial 65
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=114,
random_state=100))])
cv score: [0.64583333 0.60166667 0.72833333 0.78 0.76186709]
----------------------------------------
Trial 66
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68833333 0.68166667 0.73333333 0.835 0.85759494]
----------------------------------------
Trial 67
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=112,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59333333 0.66 0.76083333 0.77166667 0.80537975]
----------------------------------------
Trial 68
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=179, random_state=100,
subsample=0.6))])
cv score: [0.70333333 0.58083333 0.68583333 0.79166667 0.79351266]
----------------------------------------
Trial 69
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=63,
random_state=100))])
cv score: [0.69416667 0.62833333 0.6825 0.77916667 0.81724684]
----------------------------------------
Trial 70
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.71083333 0.78833333 0.83083333 0.86946203]
----------------------------------------
Trial 71
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='sqrt',
n_estimators=181, random_state=100,
subsample=0.65))])
cv score: [0.6775 0.63416667 0.715 0.81083333 0.81803797]
----------------------------------------
Trial 72
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76083333 0.70166667 0.80666667 0.82833333 0.88844937]
----------------------------------------
Trial 73
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=59,
random_state=100))])
cv score: [0.6675 0.50541667 0.795 0.66375 0.78876582]
----------------------------------------
Trial 74
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.66833333 0.79166667 0.7625 0.84335443]
----------------------------------------
Trial 75
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=110,
random_state=100))])
cv score: [0.61333333 0.67916667 0.70083333 0.81166667 0.82832278]
----------------------------------------
Trial 76
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=189, random_state=100,
subsample=0.65))])
cv score: [0.71 0.60416667 0.70166667 0.77 0.83306962]
----------------------------------------
Trial 77
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=96,
random_state=100))])
cv score: [0.6525 0.66666667 0.7 0.80666667 0.83939873]
----------------------------------------
Trial 78
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6925 0.6325 0.77416667 0.73333333 0.81566456]
----------------------------------------
Trial 79
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=17, random_state=100))])
cv score: [0.63375 0.57208333 0.6575 0.67833333 0.7721519 ]
----------------------------------------
Trial 80
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=113,
random_state=100))])
cv score: [0.68708333 0.51375 0.82458333 0.66458333 0.66574367]
----------------------------------------
Trial 81
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=186, random_state=100,
subsample=0.85))])
cv score: [0.66583333 0.6375 0.75083333 0.69833333 0.71914557]
----------------------------------------
Trial 82
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=156, random_state=100))])
cv score: [0.69583333 0.63 0.72333333 0.80833333 0.84731013]
----------------------------------------
Trial 83
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=57, random_state=100))])
cv score: [0.68625 0.60333333 0.72666667 0.81791667 0.78481013]
----------------------------------------
Trial 84
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=53,
random_state=100))])
cv score: [0.52458333 0.66875 0.70833333 0.83 0.7903481 ]
----------------------------------------
Trial 85
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=47, random_state=100,
subsample=0.7))])
cv score: [0.6175 0.62333333 0.76666667 0.82416667 0.85996835]
----------------------------------------
Trial 86
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=175, random_state=100,
subsample=0.8))])
cv score: [0.77416667 0.68416667 0.72166667 0.855 0.85443038]
----------------------------------------
Trial 87
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=97, random_state=100,
subsample=0.65))])
cv score: [0.72916667 0.71833333 0.6975 0.7775 0.66772152]
----------------------------------------
Trial 88
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=146, random_state=100))])
cv score: [0.68416667 0.66 0.72166667 0.81166667 0.85996835]
----------------------------------------
Trial 89
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=117, random_state=100))])
cv score: [0.72583333 0.67875 0.785 0.83 0.83623418]
----------------------------------------
Trial 90
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=61, random_state=100,
subsample=0.95))])
cv score: [0.68583333 0.535 0.705 0.7675 0.82674051]
----------------------------------------
Trial 91
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=64,
random_state=100))])
cv score: [0.61333333 0.52333333 0.76375 0.605 0.63765823]
----------------------------------------
Trial 92
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=151,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65833333 0.61583333 0.76166667 0.72333333 0.84889241]
----------------------------------------
Trial 93
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=27,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71208333 0.73583333 0.77666667 0.85333333 0.88449367]
----------------------------------------
Trial 94
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=35, random_state=100,
subsample=0.7))])
cv score: [0.65333333 0.50083333 0.62416667 0.45333333 0.61313291]
----------------------------------------
Trial 95
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=16, random_state=100))])
cv score: [0.59416667 0.66916667 0.69458333 0.79583333 0.8125 ]
----------------------------------------
Trial 96
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='log2',
n_estimators=94, random_state=100,
subsample=0.85))])
cv score: [0.66916667 0.595 0.61166667 0.65 0.77927215]
----------------------------------------
Trial 97
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=160,
random_state=100))])
cv score: [0.6825 0.5975 0.69583333 0.79416667 0.8125 ]
----------------------------------------
Trial 98
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='sqrt',
n_estimators=36, random_state=100,
subsample=0.65))])
cv score: [0.6025 0.54166667 0.65583333 0.6725 0.77848101]
----------------------------------------
Trial 99
----------------------------------------
Parameters {'rf__n_estimators': 125, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=125, random_state=100))])
cv score: [0.6875 0.635 0.73916667 0.81541667 0.84098101]
----------------------------------------
Trial 100
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=158,
random_state=100))])
cv score: [0.6475 0.58458333 0.73458333 0.78708333 0.78718354]
----------------------------------------
Trial 101
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=39,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66 0.67916667 0.775 0.7625 0.87420886]
----------------------------------------
Trial 102
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=50,
random_state=100,
subsample=0.85))])
cv score: [0.69833333 0.66416667 0.78333333 0.795 0.82199367]
----------------------------------------
Trial 103
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=122, random_state=100))])
cv score: [0.69333333 0.60416667 0.7425 0.82375 0.82792722]
----------------------------------------
Trial 104
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
n_estimators=107, random_state=100,
subsample=0.85))])
cv score: [0.72083333 0.64958333 0.82166667 0.83583333 0.79667722]
----------------------------------------
Trial 105
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
n_estimators=63, random_state=100,
subsample=0.85))])
cv score: [0.73083333 0.64833333 0.83208333 0.83 0.82041139]
----------------------------------------
Trial 106
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=67,
random_state=100))])
cv score: [0.67 0.58666667 0.69416667 0.805 0.84651899]
----------------------------------------
Trial 107
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=198, random_state=100))])
cv score: [0.67916667 0.62666667 0.72833333 0.79833333 0.84493671]
----------------------------------------
Trial 108
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=26, random_state=100))])
cv score: [0.66458333 0.64208333 0.705 0.79916667 0.82318038]
----------------------------------------
Trial 109
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74833333 0.6325 0.73916667 0.76291667 0.85838608]
----------------------------------------
Trial 110
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.74625 0.77 0.84416667 0.8710443 ]
----------------------------------------
Trial 111
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=95, random_state=100))])
cv score: [0.72166667 0.67291667 0.77958333 0.83416667 0.83781646]
----------------------------------------
Trial 112
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=156,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 113
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=91, random_state=100))])
cv score: [0.72708333 0.66333333 0.78041667 0.82916667 0.8306962 ]
----------------------------------------
Trial 114
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=120, random_state=100))])
cv score: [0.72333333 0.68041667 0.78375 0.82958333 0.83781646]
----------------------------------------
Trial 115
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
n_estimators=75, random_state=100,
subsample=0.8))])
cv score: [0.66333333 0.65416667 0.73916667 0.81 0.79193038]
----------------------------------------
Trial 116
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=11,
random_state=100))])
cv score: [0.74 0.54291667 0.67041667 0.74958333 0.80617089]
----------------------------------------
Trial 117
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60333333 0.6825 0.65666667 0.80375 0.75356013]
----------------------------------------
Trial 118
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68333333 0.69333333 0.79833333 0.81416667 0.89398734]
----------------------------------------
Trial 119
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='log2',
n_estimators=122, random_state=100,
subsample=0.75))])
cv score: [0.64833333 0.68166667 0.69083333 0.80083333 0.75791139]
----------------------------------------
Trial 120
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=130, random_state=100,
subsample=0.65))])
cv score: [0.60416667 0.59666667 0.69583333 0.795 0.82753165]
----------------------------------------
Trial 121
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=90, random_state=100,
subsample=0.6))])
cv score: [0.75583333 0.675 0.7675 0.84666667 0.84810127]
----------------------------------------
Trial 122
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=19, random_state=100))])
cv score: [0.64833333 0.58291667 0.70583333 0.74083333 0.75988924]
----------------------------------------
Trial 123
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=22, random_state=100))])
cv score: [0.60916667 0.6625 0.69125 0.78916667 0.84889241]
----------------------------------------
Trial 124
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='sqrt',
n_estimators=189, random_state=100,
subsample=0.9))])
cv score: [0.65083333 0.59916667 0.64083333 0.78833333 0.80696203]
----------------------------------------
Trial 125
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=58,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63333333 0.66083333 0.77916667 0.75166667 0.85759494]
----------------------------------------
Trial 126
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=17,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.82166667 0.75541667 0.79125 0.8575 0.86629747]
----------------------------------------
Trial 127
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.81083333 0.72708333 0.79291667 0.83875 0.84731013]
----------------------------------------
Trial 128
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=160, random_state=100,
subsample=0.65))])
cv score: [0.64 0.5575 0.55583333 0.65083333 0.75474684]
----------------------------------------
Trial 129
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='sqrt',
n_estimators=110, random_state=100,
subsample=0.75))])
cv score: [0.67166667 0.65916667 0.685 0.81416667 0.83860759]
----------------------------------------
Trial 130
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=123,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73333333 0.71916667 0.75333333 0.86166667 0.86787975]
----------------------------------------
Trial 131
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=95, random_state=100))])
cv score: [0.61416667 0.70083333 0.7025 0.78833333 0.83702532]
----------------------------------------
Trial 132
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=106,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61083333 0.62625 0.77583333 0.71166667 0.82832278]
----------------------------------------
Trial 133
----------------------------------------
Parameters {'rf__n_estimators': 25, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=25,
random_state=100))])
cv score: [0.685 0.49375 0.81125 0.64041667 0.75237342]
----------------------------------------
Trial 134
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=45, random_state=100,
subsample=0.6))])
cv score: [0.69666667 0.59416667 0.72333333 0.7475 0.82594937]
----------------------------------------
Trial 135
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=10,
random_state=100))])
cv score: [0.5725 0.58625 0.67583333 0.81708333 0.86313291]
----------------------------------------
Trial 136
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=155,
random_state=100, subsample=0.7))])
cv score: [0.66416667 0.65583333 0.77 0.80333333 0.83386076]
----------------------------------------
Trial 137
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=190,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62916667 0.62083333 0.735 0.74916667 0.73101266]
----------------------------------------
Trial 138
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.50416667 0.67791667 0.65958333 0.77958333 0.81685127]
----------------------------------------
Trial 139
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=114,
random_state=100))])
cv score: [0.6825 0.5975 0.68083333 0.80583333 0.83386076]
----------------------------------------
Trial 140
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='sqrt',
n_estimators=132, random_state=100,
subsample=0.8))])
cv score: [0.55583333 0.62166667 0.66416667 0.715 0.75316456]
----------------------------------------
Trial 141
----------------------------------------
Parameters {'gb__n_estimators': 55, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='log2',
n_estimators=55, random_state=100,
subsample=0.95))])
cv score: [0.65416667 0.65125 0.67666667 0.805 0.82120253]
----------------------------------------
Trial 142
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=129,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.6675 0.7575 0.74166667 0.82278481]
----------------------------------------
Trial 143
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=193, random_state=100,
subsample=0.8))])
cv score: [0.56 0.63083333 0.73916667 0.62916667 0.70490506]
----------------------------------------
Trial 144
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=84, random_state=100))])
cv score: [0.67166667 0.63583333 0.72375 0.8225 0.80617089]
----------------------------------------
Trial 145
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=186,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 146
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=19, random_state=100))])
cv score: [0.6175 0.65416667 0.69083333 0.69291667 0.83306962]
----------------------------------------
Trial 147
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=155,
random_state=100))])
cv score: [0.68666667 0.57583333 0.71333333 0.74583333 0.74683544]
----------------------------------------
Trial 148
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=115, random_state=100,
subsample=0.85))])
cv score: [0.69416667 0.5975 0.72916667 0.73 0.68591772]
----------------------------------------
Trial 149
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='log2',
n_estimators=20, random_state=100,
subsample=0.65))])
cv score: [0.65333333 0.57833333 0.71416667 0.75583333 0.8306962 ]
----------------------------------------
Trial 150
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68166667 0.65666667 0.7 0.75166667 0.80458861]
----------------------------------------
Trial 151
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
n_estimators=162, random_state=100,
subsample=0.8))])
cv score: [0.7425 0.65083333 0.79083333 0.83833333 0.80063291]
----------------------------------------
Trial 152
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=21, random_state=100,
subsample=0.75))])
cv score: [0.70333333 0.6675 0.80083333 0.81833333 0.85363924]
----------------------------------------
Trial 153
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='log2',
n_estimators=144, random_state=100,
subsample=0.7))])
cv score: [0.5925 0.63791667 0.68333333 0.7725 0.79667722]
----------------------------------------
Trial 154
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=140,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.71166667 0.76708333 0.85333333 0.8789557 ]
----------------------------------------
Trial 155
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=195, random_state=100,
subsample=0.95))])
cv score: [0.75666667 0.62916667 0.78416667 0.81083333 0.84256329]
----------------------------------------
Trial 156
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=172, random_state=100))])
cv score: [0.7225 0.67666667 0.77708333 0.835 0.82832278]
----------------------------------------
Trial 157
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=134,
random_state=100))])
cv score: [0.68666667 0.58666667 0.69833333 0.79416667 0.80300633]
----------------------------------------
Trial 158
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77125 0.73458333 0.79666667 0.82666667 0.84335443]
----------------------------------------
Trial 159
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.6825 0.78916667 0.77333333 0.85838608]
----------------------------------------
Trial 160
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70041667 0.76916667 0.77291667 0.82708333 0.88132911]
----------------------------------------
Trial 161
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=114,
random_state=100))])
cv score: [0.68708333 0.51375 0.82458333 0.66458333 0.66574367]
----------------------------------------
Trial 162
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65 0.63333333 0.7475 0.77166667 0.84018987]
----------------------------------------
Trial 163
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=33,
random_state=100))])
cv score: [0.70083333 0.5425 0.79375 0.66958333 0.63014241]
----------------------------------------
Trial 164
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.69666667 0.76583333 0.79833333 0.88212025]
----------------------------------------
Trial 165
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=188,
random_state=100))])
cv score: [0.63666667 0.57 0.73166667 0.78916667 0.80221519]
----------------------------------------
Trial 166
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=90,
random_state=100,
subsample=0.75))])
cv score: [0.6975 0.66166667 0.76916667 0.82666667 0.78639241]
----------------------------------------
Trial 167
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=82,
random_state=100))])
cv score: [0.69416667 0.57833333 0.68 0.78333333 0.79193038]
----------------------------------------
Trial 168
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=154, random_state=100,
subsample=0.6))])
cv score: [0.71 0.66916667 0.78 0.8275 0.81012658]
----------------------------------------
Trial 169
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=86,
random_state=100))])
cv score: [0.68583333 0.49375 0.80833333 0.64666667 0.75237342]
----------------------------------------
Trial 170
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=159, random_state=100))])
cv score: [0.72833333 0.67875 0.77708333 0.83916667 0.84098101]
----------------------------------------
Trial 171
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=127, random_state=100,
subsample=0.7))])
cv score: [0.61333333 0.56 0.66166667 0.70833333 0.70253165]
----------------------------------------
Trial 172
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=191, random_state=100,
subsample=0.85))])
cv score: [0.61083333 0.57666667 0.68083333 0.8025 0.72389241]
----------------------------------------
Trial 173
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.6775 0.75666667 0.78833333 0.8528481 ]
----------------------------------------
Trial 174
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='sqrt',
n_estimators=199, random_state=100,
subsample=0.8))])
cv score: [0.555 0.40625 0.63916667 0.51958333 0.35996835]
----------------------------------------
Trial 175
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=58,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62166667 0.66666667 0.72083333 0.72833333 0.77768987]
----------------------------------------
Trial 176
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=122, random_state=100,
subsample=0.65))])
cv score: [0.70083333 0.62333333 0.7325 0.805 0.84018987]
----------------------------------------
Trial 177
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.73208333 0.765 0.86083333 0.86550633]
----------------------------------------
Trial 178
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65 0.65166667 0.72166667 0.70166667 0.79351266]
----------------------------------------
Trial 179
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=53, random_state=100,
subsample=0.8))])
cv score: [0.56416667 0.59083333 0.62583333 0.51583333 0.73022152]
----------------------------------------
Trial 180
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=32,
random_state=100))])
cv score: [0.55666667 0.68083333 0.68458333 0.81708333 0.84137658]
----------------------------------------
Trial 181
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=182, random_state=100,
subsample=0.9))])
cv score: [0.76 0.63083333 0.81083333 0.83583333 0.79193038]
----------------------------------------
Trial 182
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=90, random_state=100,
subsample=0.8))])
cv score: [0.635 0.6575 0.68583333 0.8125 0.78085443]
----------------------------------------
Trial 183
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.6))])
cv score: [0.62 0.63916667 0.66583333 0.7575 0.81487342]
----------------------------------------
Trial 184
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=155,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.8375 0.82199367]
----------------------------------------
Trial 185
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=141,
random_state=100))])
cv score: [0.75 0.58791667 0.80833333 0.79208333 0.80458861]
----------------------------------------
Trial 186
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=115, random_state=100))])
cv score: [0.69 0.62833333 0.72 0.8025 0.84018987]
----------------------------------------
Trial 187
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.70333333 0.79333333 0.8025 0.86471519]
----------------------------------------
Trial 188
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65166667 0.6425 0.77666667 0.8 0.84968354]
----------------------------------------
Trial 189
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=17,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68416667 0.74041667 0.7575 0.82125 0.84335443]
----------------------------------------
Trial 190
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=113, random_state=100,
subsample=0.7))])
cv score: [0.635 0.64333333 0.71166667 0.67 0.56329114]
----------------------------------------
Trial 191
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=83,
random_state=100, subsample=0.9))])
cv score: [0.69083333 0.68083333 0.7775 0.79333333 0.80300633]
----------------------------------------
Trial 192
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=178,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69666667 0.66916667 0.77333333 0.7475 0.83386076]
----------------------------------------
Trial 193
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='log2',
n_estimators=124, random_state=100,
subsample=0.85))])
cv score: [0.54166667 0.59 0.67833333 0.66916667 0.76977848]
----------------------------------------
Trial 194
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.785 0.73666667 0.79 0.8375 0.86867089]
----------------------------------------
Trial 195
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73083333 0.6875 0.8075 0.835 0.88053797]
----------------------------------------
Trial 196
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=68, random_state=100,
subsample=0.9))])
cv score: [0.7025 0.56833333 0.64583333 0.78833333 0.80221519]
----------------------------------------
Trial 197
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=69,
random_state=100))])
cv score: [0.55708333 0.67791667 0.70958333 0.82541667 0.80023734]
----------------------------------------
Trial 198
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
n_estimators=36, random_state=100,
subsample=0.9))])
cv score: [0.68 0.64583333 0.69583333 0.83833333 0.85126582]
----------------------------------------
Trial 199
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=93, random_state=100))])
cv score: [0.73416667 0.67833333 0.7825 0.82916667 0.85126582]
----------------------------------------
Trial 200
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=102,
random_state=100))])
cv score: [0.67416667 0.5975 0.72 0.8175 0.8164557 ]
----------------------------------------
Trial 201
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=135,
random_state=100))])
cv score: [0.5825 0.665 0.70833333 0.83416667 0.82832278]
----------------------------------------
Trial 202
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=167,
random_state=100))])
cv score: [0.66916667 0.59166667 0.72125 0.79958333 0.79509494]
----------------------------------------
Trial 203
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=168, random_state=100,
subsample=0.9))])
cv score: [0.7775 0.62458333 0.80333333 0.8325 0.84177215]
----------------------------------------
Trial 204
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=110,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79666667 0.73 0.80166667 0.83833333 0.8710443 ]
----------------------------------------
Trial 205
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=133,
random_state=100))])
cv score: [0.68625 0.51375 0.82416667 0.66375 0.66416139]
----------------------------------------
Trial 206
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=105,
random_state=100))])
cv score: [0.55791667 0.65958333 0.6875 0.82708333 0.79786392]
----------------------------------------
Trial 207
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.67666667 0.77333333 0.79 0.87974684]
----------------------------------------
Trial 208
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77666667 0.735 0.78416667 0.84166667 0.87262658]
----------------------------------------
Trial 209
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=43,
random_state=100))])
cv score: [0.69 0.48916667 0.77916667 0.67166667 0.63765823]
----------------------------------------
Trial 210
----------------------------------------
Parameters {'gb__n_estimators': 177, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='log2',
n_estimators=177,
random_state=100))])
cv score: [0.665 0.6425 0.73916667 0.79916667 0.84968354]
----------------------------------------
Trial 211
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=151, random_state=100,
subsample=0.8))])
cv score: [0.76333333 0.69375 0.73 0.85416667 0.86985759]
----------------------------------------
Trial 212
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76166667 0.745 0.77333333 0.86 0.84572785]
----------------------------------------
Trial 213
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=161, random_state=100,
subsample=0.65))])
cv score: [0.72083333 0.65583333 0.75916667 0.78916667 0.77768987]
----------------------------------------
Trial 214
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=143, random_state=100,
subsample=0.65))])
cv score: [0.7075 0.60166667 0.71083333 0.76083333 0.83386076]
----------------------------------------
Trial 215
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=11, random_state=100))])
cv score: [0.59041667 0.60083333 0.68458333 0.72625 0.79153481]
----------------------------------------
Trial 216
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72083333 0.66083333 0.7625 0.77083333 0.82357595]
----------------------------------------
Trial 217
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=60,
random_state=100))])
cv score: [0.73333333 0.7325 0.75916667 0.85875 0.88014241]
----------------------------------------
Trial 218
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69833333 0.72208333 0.75625 0.84833333 0.88449367]
----------------------------------------
Trial 219
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72833333 0.70916667 0.79333333 0.825 0.88132911]
----------------------------------------
Trial 220
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=169, random_state=100))])
cv score: [0.68333333 0.63333333 0.7425 0.8225 0.81803797]
----------------------------------------
Trial 221
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67833333 0.65333333 0.79666667 0.81166667 0.88132911]
----------------------------------------
Trial 222
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=161,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65583333 0.65583333 0.74583333 0.77333333 0.79984177]
----------------------------------------
Trial 223
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=113,
random_state=100))])
cv score: [0.69333333 0.48916667 0.77916667 0.67083333 0.63844937]
----------------------------------------
Trial 224
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=64, random_state=100,
subsample=0.85))])
cv score: [0.66083333 0.55666667 0.66333333 0.71333333 0.73575949]
----------------------------------------
Trial 225
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=179,
random_state=100))])
cv score: [0.75041667 0.58791667 0.80833333 0.79208333 0.80458861]
----------------------------------------
Trial 226
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='sqrt',
n_estimators=72,
random_state=100))])
cv score: [0.67583333 0.61 0.70666667 0.6675 0.74683544]
----------------------------------------
Trial 227
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=163,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7275 0.67166667 0.78916667 0.78166667 0.82436709]
----------------------------------------
Trial 228
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.6))])
cv score: [0.67333333 0.64666667 0.70333333 0.78583333 0.83781646]
----------------------------------------
Trial 229
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=92, random_state=100))])
cv score: [0.73 0.66083333 0.7775 0.84458333 0.83346519]
----------------------------------------
Trial 230
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=90,
random_state=100))])
cv score: [0.675 0.59333333 0.67916667 0.81333333 0.83860759]
----------------------------------------
Trial 231
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=106,
random_state=100))])
cv score: [0.61416667 0.54 0.70916667 0.705 0.72705696]
----------------------------------------
Trial 232
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=169,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5425 0.59 0.735 0.7025 0.75553797]
----------------------------------------
Trial 233
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68 0.6725 0.78583333 0.78416667 0.89003165]
----------------------------------------
Trial 234
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=67, random_state=100,
subsample=0.85))])
cv score: [0.60333333 0.54416667 0.74083333 0.61916667 0.65189873]
----------------------------------------
Trial 235
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.74208333 0.77833333 0.84916667 0.86392405]
----------------------------------------
Trial 236
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=182,
random_state=100))])
cv score: [0.67333333 0.57583333 0.7175 0.81916667 0.83623418]
----------------------------------------
Trial 237
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=192, random_state=100))])
cv score: [0.7025 0.61 0.73416667 0.82041667 0.80063291]
----------------------------------------
Trial 238
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
n_estimators=101, random_state=100,
subsample=0.6))])
cv score: [0.76833333 0.6825 0.7775 0.84333333 0.84651899]
----------------------------------------
Trial 239
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6175 0.67166667 0.77833333 0.73916667 0.85205696]
----------------------------------------
Trial 240
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65833333 0.67833333 0.77833333 0.7625 0.8306962 ]
----------------------------------------
Trial 241
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=60, random_state=100,
subsample=0.7))])
cv score: [0.6875 0.58333333 0.6775 0.76833333 0.79825949]
----------------------------------------
Trial 242
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=62, random_state=100))])
cv score: [0.64833333 0.65583333 0.695 0.82583333 0.85522152]
----------------------------------------
Trial 243
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67 0.63583333 0.73666667 0.775 0.8306962 ]
----------------------------------------
Trial 244
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=191,
random_state=100))])
cv score: [0.67666667 0.57416667 0.72083333 0.8175 0.84098101]
----------------------------------------
Trial 245
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='sqrt',
n_estimators=110, random_state=100,
subsample=0.85))])
cv score: [0.6075 0.5275 0.4775 0.68333333 0.6835443 ]
----------------------------------------
Trial 246
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='sqrt',
n_estimators=58, random_state=100,
subsample=0.6))])
cv score: [0.71166667 0.66166667 0.72833333 0.83333333 0.8164557 ]
----------------------------------------
Trial 247
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.9))])
cv score: [0.66083333 0.61666667 0.70833333 0.74916667 0.8306962 ]
----------------------------------------
Trial 248
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=151,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.765 0.7275 0.77875 0.84416667 0.87341772]
----------------------------------------
Trial 249
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
n_estimators=130, random_state=100,
subsample=0.8))])
cv score: [0.575 0.6275 0.71666667 0.80416667 0.75079114]
----------------------------------------
Trial 250
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=62,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6725 0.6525 0.79833333 0.78583333 0.86392405]
----------------------------------------
Trial 251
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73833333 0.71333333 0.73375 0.86083333 0.8346519 ]
----------------------------------------
Trial 252
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60083333 0.69583333 0.755 0.74166667 0.82278481]
----------------------------------------
Trial 253
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=73,
random_state=100, subsample=0.6))])
cv score: [0.70166667 0.66 0.7775 0.83666667 0.79667722]
----------------------------------------
Trial 254
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=44, random_state=100,
subsample=0.95))])
cv score: [0.7525 0.65083333 0.7525 0.78416667 0.75237342]
----------------------------------------
Trial 255
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, n_estimators=174,
random_state=100))])
cv score: [0.65458333 0.57625 0.79166667 0.70416667 0.73259494]
----------------------------------------
Trial 256
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57333333 0.67083333 0.7775 0.61708333 0.71756329]
----------------------------------------
Trial 257
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=31, random_state=100))])
cv score: [0.71416667 0.67875 0.77583333 0.85125 0.85719937]
----------------------------------------
Trial 258
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
n_estimators=120, random_state=100,
subsample=0.6))])
cv score: [0.71916667 0.68083333 0.735 0.81416667 0.85126582]
----------------------------------------
Trial 259
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=48, random_state=100))])
cv score: [0.72583333 0.66 0.78875 0.83625 0.8346519 ]
----------------------------------------
Trial 260
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.9))])
cv score: [0.61083333 0.56083333 0.67 0.71083333 0.75 ]
----------------------------------------
Trial 261
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=162, random_state=100,
subsample=0.85))])
cv score: [0.6575 0.60333333 0.69666667 0.79583333 0.79588608]
----------------------------------------
Trial 262
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=65, random_state=100,
subsample=0.6))])
cv score: [0.65916667 0.64666667 0.6775 0.78166667 0.82674051]
----------------------------------------
Trial 263
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=17,
random_state=100))])
cv score: [0.62916667 0.66041667 0.67791667 0.79875 0.83781646]
----------------------------------------
Trial 264
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=96, random_state=100,
subsample=0.85))])
cv score: [0.67166667 0.57666667 0.69416667 0.77833333 0.76265823]
----------------------------------------
Trial 265
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=176, random_state=100,
subsample=0.6))])
cv score: [0.68083333 0.63833333 0.685 0.76416667 0.79905063]
----------------------------------------
Trial 266
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64 0.6425 0.76583333 0.77583333 0.84256329]
----------------------------------------
Trial 267
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=64, random_state=100))])
cv score: [0.74166667 0.69666667 0.7425 0.85166667 0.88844937]
----------------------------------------
Trial 268
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(n_estimators=130, random_state=100,
subsample=0.7))])
cv score: [0.68833333 0.70583333 0.7575 0.79333333 0.82911392]
----------------------------------------
Trial 269
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=37,
random_state=100))])
cv score: [0.55666667 0.67916667 0.70041667 0.82125 0.84137658]
----------------------------------------
Trial 270
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=93, random_state=100))])
cv score: [0.66666667 0.665 0.69 0.8225 0.86787975]
----------------------------------------
Trial 271
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='log2',
n_estimators=143, random_state=100,
subsample=0.75))])
cv score: [0.64166667 0.65166667 0.68166667 0.78083333 0.77848101]
----------------------------------------
Trial 272
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=152,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66916667 0.65333333 0.76416667 0.80416667 0.85601266]
----------------------------------------
Trial 273
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=33, random_state=100,
subsample=0.6))])
cv score: [0.69083333 0.5775 0.6725 0.7975 0.77136076]
----------------------------------------
Trial 274
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=59, random_state=100))])
cv score: [0.65916667 0.62708333 0.73125 0.83916667 0.78283228]
----------------------------------------
Trial 275
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=21, random_state=100))])
cv score: [0.69375 0.68291667 0.69458333 0.86625 0.86075949]
----------------------------------------
Trial 276
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59666667 0.72583333 0.7225 0.86166667 0.77056962]
----------------------------------------
Trial 277
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=181, random_state=100,
subsample=0.85))])
cv score: [0.69166667 0.6075 0.67166667 0.79583333 0.8164557 ]
----------------------------------------
Trial 278
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=40, random_state=100))])
cv score: [0.72416667 0.66291667 0.76291667 0.84541667 0.84612342]
----------------------------------------
Trial 279
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=120, random_state=100))])
cv score: [0.69083333 0.63166667 0.71916667 0.8025 0.84335443]
----------------------------------------
Trial 280
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=69,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70791667 0.72625 0.77708333 0.83833333 0.86748418]
----------------------------------------
Trial 281
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=158,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 282
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=186,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79916667 0.66291667 0.78876582]
----------------------------------------
Trial 283
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=104,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66583333 0.65083333 0.79083333 0.74083333 0.78560127]
----------------------------------------
Trial 284
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=178,
random_state=100))])
cv score: [0.74208333 0.58875 0.75916667 0.76416667 0.67128165]
----------------------------------------
Trial 285
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=198, random_state=100))])
cv score: [0.69583333 0.61333333 0.735 0.82041667 0.80023734]
----------------------------------------
Trial 286
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.72 0.7975 0.8125 0.85363924]
----------------------------------------
Trial 287
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
n_estimators=170, random_state=100,
subsample=0.9))])
cv score: [0.79666667 0.66416667 0.78333333 0.86333333 0.84335443]
----------------------------------------
Trial 288
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='log2',
n_estimators=181, random_state=100,
subsample=0.65))])
cv score: [0.70583333 0.6225 0.7325 0.80666667 0.79667722]
----------------------------------------
Trial 289
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=123,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67083333 0.66416667 0.77416667 0.78833333 0.84572785]
----------------------------------------
Trial 290
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=111,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59666667 0.65416667 0.78833333 0.735 0.79825949]
----------------------------------------
Trial 291
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6725 0.6075 0.73916667 0.68833333 0.67484177]
----------------------------------------
Trial 292
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=140, random_state=100))])
cv score: [0.67 0.65666667 0.70916667 0.81916667 0.85443038]
----------------------------------------
Trial 293
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.775 0.72833333 0.7925 0.84916667 0.88053797]
----------------------------------------
Trial 294
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
n_estimators=106, random_state=100,
subsample=0.8))])
cv score: [0.72416667 0.64791667 0.82 0.8425 0.79825949]
----------------------------------------
Trial 295
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=192,
random_state=100))])
cv score: [0.63666667 0.57458333 0.73 0.7925 0.80379747]
----------------------------------------
Trial 296
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.725 0.79416667 0.86166667 0.87737342]
----------------------------------------
Trial 297
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=174,
random_state=100,
subsample=0.95))])
cv score: [0.7175 0.62416667 0.815 0.81333333 0.8085443 ]
----------------------------------------
Trial 298
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=133, random_state=100))])
cv score: [0.6875 0.63833333 0.74083333 0.81291667 0.82990506]
----------------------------------------
Trial 299
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=168, random_state=100,
subsample=0.7))])
cv score: [0.73666667 0.59833333 0.69166667 0.82666667 0.8085443 ]
----------------------------------------
Trial 300
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=32,
random_state=100, subsample=0.9))])
cv score: [0.73666667 0.58333333 0.83625 0.785 0.74920886]
----------------------------------------
Trial 301
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=190,
random_state=100,
subsample=0.95))])
cv score: [0.63833333 0.62416667 0.69583333 0.7475 0.70174051]
----------------------------------------
Trial 302
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=19, random_state=100))])
cv score: [0.73333333 0.67 0.7675 0.87708333 0.84493671]
----------------------------------------
Trial 303
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=191,
random_state=100))])
cv score: [0.685 0.6325 0.71416667 0.8025 0.84493671]
----------------------------------------
Trial 304
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=195,
random_state=100))])
cv score: [0.67666667 0.57833333 0.72083333 0.81541667 0.84256329]
----------------------------------------
Trial 305
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=162, random_state=100))])
cv score: [0.69625 0.61583333 0.74333333 0.82208333 0.79707278]
----------------------------------------
Trial 306
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66833333 0.64416667 0.7475 0.74083333 0.73892405]
----------------------------------------
Trial 307
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=137,
random_state=100))])
cv score: [0.51541667 0.67958333 0.70791667 0.82416667 0.77373418]
----------------------------------------
Trial 308
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='log2',
n_estimators=71, random_state=100,
subsample=0.8))])
cv score: [0.68083333 0.71791667 0.7 0.83833333 0.8125 ]
----------------------------------------
Trial 309
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=14, random_state=100))])
cv score: [0.7125 0.64916667 0.77125 0.86333333 0.81803797]
----------------------------------------
Trial 310
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=155, random_state=100,
subsample=0.65))])
cv score: [0.6775 0.7125 0.68416667 0.77583333 0.79193038]
----------------------------------------
Trial 311
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=171,
random_state=100))])
cv score: [0.70833333 0.6025 0.6725 0.80833333 0.83702532]
----------------------------------------
Trial 312
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.7))])
cv score: [0.66666667 0.665 0.74333333 0.8125 0.8164557 ]
----------------------------------------
Trial 313
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=185, random_state=100,
subsample=0.85))])
cv score: [0.6575 0.61333333 0.67583333 0.77333333 0.78085443]
----------------------------------------
Trial 314
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=68, random_state=100))])
cv score: [0.65583333 0.64833333 0.70166667 0.83083333 0.85363924]
----------------------------------------
Trial 315
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='log2',
n_estimators=146, random_state=100,
subsample=0.85))])
cv score: [0.65083333 0.65 0.70083333 0.83833333 0.8306962 ]
----------------------------------------
Trial 316
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=22, random_state=100))])
cv score: [0.71541667 0.65416667 0.76166667 0.85166667 0.83188291]
----------------------------------------
Trial 317
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63916667 0.61041667 0.69416667 0.70833333 0.72151899]
----------------------------------------
Trial 318
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.72 0.80083333 0.8475 0.88528481]
----------------------------------------
Trial 319
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=119, random_state=100))])
cv score: [0.6125 0.6975 0.70416667 0.79833333 0.82674051]
----------------------------------------
Trial 320
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6175 0.615 0.74666667 0.76083333 0.75712025]
----------------------------------------
Trial 321
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75666667 0.72083333 0.7725 0.82166667 0.87025316]
----------------------------------------
Trial 322
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=60,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69166667 0.69 0.775 0.78666667 0.86234177]
----------------------------------------
Trial 323
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=148, random_state=100))])
cv score: [0.6925 0.63083333 0.73916667 0.79666667 0.82594937]
----------------------------------------
Trial 324
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=32,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67666667 0.665 0.79083333 0.73416667 0.82357595]
----------------------------------------
Trial 325
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.69083333 0.79916667 0.79 0.86392405]
----------------------------------------
Trial 326
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=134,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7725 0.7225 0.75833333 0.8325 0.87974684]
----------------------------------------
Trial 327
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=49,
random_state=100))])
cv score: [0.66708333 0.59125 0.72708333 0.77041667 0.78560127]
----------------------------------------
Trial 328
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=82,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71166667 0.6825 0.785 0.78166667 0.88132911]
----------------------------------------
Trial 329
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=32,
random_state=100))])
cv score: [0.72333333 0.58416667 0.70916667 0.73416667 0.77689873]
----------------------------------------
Trial 330
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=150, random_state=100))])
cv score: [0.62 0.69 0.70333333 0.80083333 0.83227848]
----------------------------------------
Trial 331
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
n_estimators=51, random_state=100,
subsample=0.6))])
cv score: [0.68333333 0.6525 0.74833333 0.83833333 0.76344937]
----------------------------------------
Trial 332
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=10, random_state=100))])
cv score: [0.66916667 0.6375 0.72208333 0.81708333 0.83386076]
----------------------------------------
Trial 333
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=128, random_state=100,
subsample=0.75))])
cv score: [0.69833333 0.65083333 0.7775 0.785 0.82357595]
----------------------------------------
Trial 334
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=46,
random_state=100))])
cv score: [0.67333333 0.58083333 0.69666667 0.79541667 0.83939873]
----------------------------------------
Trial 335
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=48, random_state=100,
subsample=0.7))])
cv score: [0.6375 0.66333333 0.72333333 0.75583333 0.82041139]
----------------------------------------
Trial 336
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=178,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7575 0.71833333 0.78083333 0.84416667 0.87262658]
----------------------------------------
Trial 337
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=90,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6075 0.71333333 0.76583333 0.73 0.86392405]
----------------------------------------
Trial 338
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
random_state=100,
subsample=0.95))])
cv score: [0.6575 0.61666667 0.6925 0.7525 0.7903481 ]
----------------------------------------
Trial 339
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=183,
random_state=100))])
cv score: [0.67666667 0.57666667 0.72 0.81833333 0.83939873]
----------------------------------------
Trial 340
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=69, random_state=100,
subsample=0.85))])
cv score: [0.5275 0.5325 0.6475 0.71166667 0.67009494]
----------------------------------------
Trial 341
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79166667 0.7425 0.7975 0.84833333 0.86946203]
----------------------------------------
Trial 342
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64 0.65666667 0.7975 0.73416667 0.79113924]
----------------------------------------
Trial 343
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=111,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.675 0.69083333 0.74083333 0.7675 0.81724684]
----------------------------------------
Trial 344
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='sqrt',
n_estimators=171, random_state=100,
subsample=0.65))])
cv score: [0.68333333 0.6275 0.6325 0.71 0.6914557 ]
----------------------------------------
Trial 345
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=51, random_state=100,
subsample=0.95))])
cv score: [0.7 0.54916667 0.745 0.79083333 0.75949367]
----------------------------------------
Trial 346
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='sqrt',
n_estimators=11, random_state=100,
subsample=0.65))])
cv score: [0.71166667 0.585 0.68833333 0.75083333 0.81091772]
----------------------------------------
Trial 347
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=103, random_state=100))])
cv score: [0.745 0.67833333 0.7825 0.835 0.84968354]
----------------------------------------
Trial 348
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=51, random_state=100))])
cv score: [0.66333333 0.63333333 0.7375 0.79166667 0.84335443]
----------------------------------------
Trial 349
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=129, random_state=100))])
cv score: [0.67833333 0.62708333 0.75666667 0.82416667 0.82871835]
----------------------------------------
Trial 350
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=182, random_state=100,
subsample=0.95))])
cv score: [0.5225 0.54416667 0.68833333 0.715 0.73417722]
----------------------------------------
Trial 351
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72541667 0.735 0.78833333 0.85125 0.85245253]
----------------------------------------
Trial 352
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=36, random_state=100,
subsample=0.95))])
cv score: [0.68666667 0.55333333 0.6875 0.775 0.83781646]
----------------------------------------
Trial 353
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=35,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.66833333 0.74083333 0.76 0.79825949]
----------------------------------------
Trial 354
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63208333 0.7425 0.78333333 0.86458333 0.84454114]
----------------------------------------
Trial 355
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58916667 0.63166667 0.79833333 0.755 0.85205696]
----------------------------------------
Trial 356
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=173, random_state=100,
subsample=0.8))])
cv score: [0.6925 0.5975 0.705 0.80583333 0.80775316]
----------------------------------------
Trial 357
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=181,
random_state=100))])
cv score: [0.56791667 0.66125 0.70916667 0.82375 0.8125 ]
----------------------------------------
Trial 358
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=143, random_state=100))])
cv score: [0.68833333 0.65583333 0.725 0.81416667 0.85917722]
----------------------------------------
Trial 359
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=119, random_state=100))])
cv score: [0.6975 0.60166667 0.74416667 0.82791667 0.83188291]
----------------------------------------
Trial 360
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='log2',
n_estimators=96, random_state=100,
subsample=0.8))])
cv score: [0.6225 0.69833333 0.68916667 0.79916667 0.79746835]
----------------------------------------
Trial 361
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='sqrt',
n_estimators=10, random_state=100,
subsample=0.95))])
cv score: [0.60166667 0.5825 0.75583333 0.7525 0.81487342]
----------------------------------------
Trial 362
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=108, random_state=100))])
cv score: [0.7425 0.67708333 0.78166667 0.83333333 0.84968354]
----------------------------------------
Trial 363
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=178, random_state=100))])
cv score: [0.5725 0.7 0.7 0.81333333 0.8125 ]
----------------------------------------
Trial 364
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
n_estimators=154, random_state=100,
subsample=0.7))])
cv score: [0.64333333 0.6575 0.7175 0.80833333 0.76028481]
----------------------------------------
Trial 365
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=143, random_state=100))])
cv score: [0.69416667 0.62666667 0.73833333 0.79333333 0.82594937]
----------------------------------------
Trial 366
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=137,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74833333 0.71583333 0.79083333 0.8575 0.875 ]
----------------------------------------
Trial 367
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7125 0.68333333 0.79166667 0.78083333 0.85917722]
----------------------------------------
Trial 368
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=22,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76041667 0.74416667 0.7725 0.84458333 0.82674051]
----------------------------------------
Trial 369
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=16, random_state=100))])
cv score: [0.6375 0.58291667 0.67625 0.6925 0.76503165]
----------------------------------------
Trial 370
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.66583333 0.79083333 0.76916667 0.83781646]
----------------------------------------
Trial 371
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='sqrt',
n_estimators=70, random_state=100,
subsample=0.75))])
cv score: [0.68 0.67 0.70166667 0.83083333 0.83148734]
----------------------------------------
Trial 372
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=29,
random_state=100))])
cv score: [0.68083333 0.51458333 0.82375 0.66791667 0.66653481]
----------------------------------------
Trial 373
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=155, random_state=100,
subsample=0.75))])
cv score: [0.55083333 0.60333333 0.67583333 0.6875 0.80221519]
----------------------------------------
Trial 374
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=47,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75666667 0.7475 0.78041667 0.86083333 0.88686709]
----------------------------------------
Trial 375
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=176, random_state=100))])
cv score: [0.605 0.69333333 0.70333333 0.805 0.83781646]
----------------------------------------
Trial 376
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=131, random_state=100))])
cv score: [0.76833333 0.68166667 0.7525 0.85083333 0.89003165]
----------------------------------------
Trial 377
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
n_estimators=26, random_state=100,
subsample=0.8))])
cv score: [0.71 0.66666667 0.79083333 0.81666667 0.87341772]
----------------------------------------
Trial 378
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=95,
random_state=100))])
cv score: [0.68833333 0.51375 0.82375 0.6625 0.66495253]
----------------------------------------
Trial 379
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=12,
random_state=100))])
cv score: [0.57833333 0.58 0.65625 0.81958333 0.85838608]
----------------------------------------
Trial 380
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=147,
random_state=100))])
cv score: [0.73375 0.56791667 0.78083333 0.80666667 0.80261076]
----------------------------------------
Trial 381
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=86, random_state=100))])
cv score: [0.6575 0.62583333 0.7525 0.80583333 0.82278481]
----------------------------------------
Trial 382
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.9))])
cv score: [0.65833333 0.62333333 0.69416667 0.78416667 0.80933544]
----------------------------------------
Trial 383
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=73,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71125 0.73083333 0.76625 0.86166667 0.85205696]
----------------------------------------
Trial 384
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=55, random_state=100))])
cv score: [0.70875 0.69416667 0.71 0.845 0.8789557 ]
----------------------------------------
Trial 385
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=155, random_state=100))])
cv score: [0.70083333 0.6325 0.74166667 0.78916667 0.82041139]
----------------------------------------
Trial 386
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.605 0.64 0.70916667 0.775 0.75158228]
----------------------------------------
Trial 387
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=154, random_state=100))])
cv score: [0.7225 0.675 0.77666667 0.83541667 0.8306962 ]
----------------------------------------
Trial 388
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=172,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74416667 0.75416667 0.78375 0.86916667 0.85838608]
----------------------------------------
Trial 389
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=169,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77333333 0.7225 0.7925 0.84666667 0.88449367]
----------------------------------------
Trial 390
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=110,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74833333 0.735 0.7725 0.84666667 0.8710443 ]
----------------------------------------
Trial 391
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=126,
random_state=100))])
cv score: [0.67083333 0.58833333 0.70666667 0.80416667 0.81803797]
----------------------------------------
Trial 392
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73583333 0.725 0.75125 0.84666667 0.87737342]
----------------------------------------
Trial 393
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73333333 0.7325 0.74125 0.86 0.88765823]
----------------------------------------
Trial 394
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=190,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6475 0.6025 0.67166667 0.68166667 0.80617089]
----------------------------------------
Trial 395
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=168,
random_state=100))])
cv score: [0.65166667 0.6725 0.69833333 0.82333333 0.84414557]
----------------------------------------
Trial 396
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76333333 0.71333333 0.78 0.84416667 0.875 ]
----------------------------------------
Trial 397
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.65 0.78416667 0.79083333 0.85601266]
----------------------------------------
Trial 398
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=103,
random_state=100))])
cv score: [0.69333333 0.4875 0.72208333 0.66916667 0.63568038]
----------------------------------------
Trial 399
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=166,
random_state=100))])
cv score: [0.62083333 0.67166667 0.7 0.82 0.83306962]
----------------------------------------
Trial 400
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=111, random_state=100,
subsample=0.75))])
cv score: [0.71 0.71083333 0.72333333 0.835 0.89556962]
----------------------------------------
Trial 401
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63083333 0.64583333 0.76333333 0.77 0.81566456]
----------------------------------------
Trial 402
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=54,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.68916667 0.78583333 0.8425 0.90664557]
----------------------------------------
Trial 403
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=104, random_state=100,
subsample=0.75))])
cv score: [0.71416667 0.68666667 0.7825 0.81916667 0.83306962]
----------------------------------------
Trial 404
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=89, random_state=100))])
cv score: [0.56916667 0.7325 0.69416667 0.795 0.80379747]
----------------------------------------
Trial 405
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.80375 0.72 0.78083333 0.84958333 0.88488924]
----------------------------------------
Trial 406
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=145, random_state=100,
subsample=0.6))])
cv score: [0.65833333 0.4675 0.56916667 0.54416667 0.71993671]
----------------------------------------
Trial 407
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=155,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.8375 0.82199367]
----------------------------------------
Trial 408
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=186, random_state=100))])
cv score: [0.78 0.695 0.7775 0.85333333 0.8710443 ]
----------------------------------------
Trial 409
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(n_estimators=31, random_state=100,
subsample=0.9))])
cv score: [0.7425 0.68 0.75833333 0.85916667 0.88607595]
----------------------------------------
Trial 410
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=119, random_state=100,
subsample=0.7))])
cv score: [0.69 0.6525 0.72583333 0.63583333 0.63844937]
----------------------------------------
Trial 411
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=16, random_state=100,
subsample=0.75))])
cv score: [0.5975 0.59 0.70666667 0.76583333 0.72151899]
----------------------------------------
Trial 412
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=108, random_state=100,
subsample=0.6))])
cv score: [0.68166667 0.67 0.77916667 0.83083333 0.79984177]
----------------------------------------
Trial 413
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63333333 0.64833333 0.74083333 0.78416667 0.83306962]
----------------------------------------
Trial 414
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.8))])
cv score: [0.6 0.65166667 0.74833333 0.73333333 0.5585443 ]
----------------------------------------
Trial 415
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=160,
random_state=100))])
cv score: [0.64833333 0.58166667 0.73458333 0.78166667 0.78797468]
----------------------------------------
Trial 416
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62833333 0.72791667 0.71833333 0.83375 0.8215981 ]
----------------------------------------
Trial 417
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=127, random_state=100,
subsample=0.85))])
cv score: [0.65166667 0.58583333 0.69583333 0.7225 0.7460443 ]
----------------------------------------
Trial 418
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=52, random_state=100,
subsample=0.8))])
cv score: [0.5275 0.59083333 0.68666667 0.7475 0.74525316]
----------------------------------------
Trial 419
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=130,
random_state=100))])
cv score: [0.51375 0.67458333 0.70291667 0.81 0.76344937]
----------------------------------------
Trial 420
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=12, random_state=100,
subsample=0.95))])
cv score: [0.7025 0.59583333 0.76666667 0.77833333 0.79272152]
----------------------------------------
Trial 421
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=187,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7225 0.68083333 0.80666667 0.81916667 0.88844937]
----------------------------------------
Trial 422
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=35, random_state=100))])
cv score: [0.72375 0.66583333 0.77833333 0.84125 0.84058544]
----------------------------------------
Trial 423
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
n_estimators=176, random_state=100,
subsample=0.9))])
cv score: [0.6775 0.6725 0.71833333 0.71666667 0.65664557]
----------------------------------------
Trial 424
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7425 0.72125 0.78083333 0.825 0.83939873]
----------------------------------------
Trial 425
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=28, random_state=100,
subsample=0.6))])
cv score: [0.71833333 0.655 0.71833333 0.77666667 0.84098101]
----------------------------------------
Trial 426
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.74375 0.79666667 0.84 0.83702532]
----------------------------------------
Trial 427
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=192, random_state=100))])
cv score: [0.67583333 0.62416667 0.73 0.79666667 0.84018987]
----------------------------------------
Trial 428
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
n_estimators=31, random_state=100,
subsample=0.9))])
cv score: [0.72333333 0.68833333 0.77333333 0.8075 0.78718354]
----------------------------------------
Trial 429
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=51, random_state=100,
subsample=0.9))])
cv score: [0.71916667 0.57083333 0.75583333 0.77833333 0.82278481]
----------------------------------------
Trial 430
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='sqrt',
n_estimators=176, random_state=100,
subsample=0.95))])
cv score: [0.65916667 0.5825 0.67916667 0.71333333 0.72863924]
----------------------------------------
Trial 431
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=169,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78333333 0.71708333 0.78583333 0.84 0.86708861]
----------------------------------------
Trial 432
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=31, random_state=100))])
cv score: [0.61791667 0.60708333 0.76458333 0.82833333 0.81566456]
----------------------------------------
Trial 433
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73333333 0.71916667 0.79666667 0.80833333 0.84018987]
----------------------------------------
Trial 434
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69291667 0.73958333 0.77125 0.86416667 0.8528481 ]
----------------------------------------
Trial 435
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=117,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71666667 0.6725 0.80083333 0.81833333 0.89398734]
----------------------------------------
Trial 436
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7575 0.74 0.79333333 0.8575 0.87262658]
----------------------------------------
Trial 437
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=59, random_state=100,
subsample=0.8))])
cv score: [0.7025 0.62583333 0.6475 0.825 0.86946203]
----------------------------------------
Trial 438
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=186,
random_state=100))])
cv score: [0.67083333 0.62166667 0.70166667 0.8025 0.82990506]
----------------------------------------
Trial 439
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
n_estimators=101,
random_state=100))])
cv score: [0.65375 0.545 0.57791667 0.75041667 0.71914557]
----------------------------------------
Trial 440
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=94, random_state=100))])
cv score: [0.61416667 0.70333333 0.7025 0.79166667 0.83939873]
----------------------------------------
Trial 441
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63166667 0.6925 0.80416667 0.73833333 0.88765823]
----------------------------------------
Trial 442
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74916667 0.7275 0.77458333 0.8575 0.86629747]
----------------------------------------
Trial 443
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.80458333 0.75458333 0.75166667 0.84916667 0.90466772]
----------------------------------------
Trial 444
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65333333 0.65333333 0.7825 0.78083333 0.83148734]
----------------------------------------
Trial 445
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=137,
random_state=100))])
cv score: [0.53083333 0.5675 0.6475 0.70416667 0.74920886]
----------------------------------------
Trial 446
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74583333 0.74416667 0.80333333 0.86083333 0.86629747]
----------------------------------------
Trial 447
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=32,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6225 0.63166667 0.68083333 0.6775 0.80063291]
----------------------------------------
Trial 448
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=41,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78333333 0.73708333 0.79833333 0.8375 0.87737342]
----------------------------------------
Trial 449
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=178, random_state=100))])
cv score: [0.69166667 0.6225 0.74166667 0.8225 0.82318038]
----------------------------------------
Trial 450
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=29,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.7125 0.79083333 0.8175 0.87737342]
----------------------------------------
Trial 451
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=194, random_state=100))])
cv score: [0.67666667 0.62666667 0.72833333 0.80083333 0.83781646]
----------------------------------------
Trial 452
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=140,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7425 0.72166667 0.77125 0.85916667 0.86075949]
----------------------------------------
Trial 453
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=15, random_state=100,
subsample=0.9))])
cv score: [0.67583333 0.61041667 0.7375 0.70666667 0.8085443 ]
----------------------------------------
Trial 454
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=161, random_state=100))])
cv score: [0.69083333 0.63083333 0.71916667 0.81416667 0.84256329]
----------------------------------------
Trial 455
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=147,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61916667 0.64083333 0.78333333 0.745 0.79588608]
----------------------------------------
Trial 456
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=39,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.76 0.7925 0.85833333 0.85047468]
----------------------------------------
Trial 457
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.635 0.64166667 0.7675 0.765 0.83227848]
----------------------------------------
Trial 458
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
n_estimators=142, random_state=100,
subsample=0.65))])
cv score: [0.57833333 0.5875 0.6875 0.625 0.67009494]
----------------------------------------
Trial 459
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.72583333 0.78375 0.85166667 0.8710443 ]
----------------------------------------
Trial 460
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=80,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.765 0.71083333 0.78833333 0.84166667 0.87658228]
----------------------------------------
Trial 461
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61583333 0.67833333 0.79 0.78 0.82278481]
----------------------------------------
Trial 462
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=23, random_state=100))])
cv score: [0.67958333 0.60416667 0.69583333 0.73375 0.81408228]
----------------------------------------
Trial 463
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=155, random_state=100))])
cv score: [0.6975 0.62833333 0.72333333 0.8075 0.84810127]
----------------------------------------
Trial 464
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=185, random_state=100,
subsample=0.8))])
cv score: [0.69 0.6025 0.72916667 0.82333333 0.79667722]
----------------------------------------
Trial 465
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=17,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78333333 0.72041667 0.76958333 0.83583333 0.90268987]
----------------------------------------
Trial 466
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='log2',
n_estimators=28,
random_state=100))])
cv score: [0.62416667 0.64166667 0.72083333 0.77291667 0.84493671]
----------------------------------------
Trial 467
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=68, random_state=100,
subsample=0.6))])
cv score: [0.7125 0.68416667 0.62333333 0.80416667 0.76898734]
----------------------------------------
Trial 468
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=61, random_state=100,
subsample=0.95))])
cv score: [0.62666667 0.55916667 0.65833333 0.76916667 0.76107595]
----------------------------------------
Trial 469
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=119, random_state=100,
subsample=0.95))])
cv score: [0.69416667 0.57583333 0.73083333 0.8225 0.81724684]
----------------------------------------
Trial 470
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='log2',
n_estimators=41, random_state=100,
subsample=0.65))])
cv score: [0.7075 0.545 0.63416667 0.60833333 0.75632911]
----------------------------------------
Trial 471
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=53, random_state=100,
subsample=0.65))])
cv score: [0.675 0.6925 0.7275 0.7675 0.82911392]
----------------------------------------
Trial 472
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=143,
random_state=100))])
cv score: [0.56041667 0.66791667 0.69083333 0.82708333 0.80617089]
----------------------------------------
Trial 473
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=79,
random_state=100))])
cv score: [0.75083333 0.58791667 0.80833333 0.7925 0.80458861]
----------------------------------------
Trial 474
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64 0.6 0.7325 0.67583333 0.85205696]
----------------------------------------
Trial 475
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=29,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68041667 0.75166667 0.77083333 0.82833333 0.83742089]
----------------------------------------
Trial 476
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=71,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77333333 0.71166667 0.80833333 0.8375 0.87737342]
----------------------------------------
Trial 477
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.54875 0.75708333 0.72375 0.79416667 0.80142405]
----------------------------------------
Trial 478
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.75))])
cv score: [0.52875 0.69333333 0.66875 0.78583333 0.85403481]
----------------------------------------
Trial 479
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70833333 0.74 0.7775 0.84666667 0.87816456]
----------------------------------------
Trial 480
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=189,
random_state=100))])
cv score: [0.68708333 0.51375 0.82458333 0.66458333 0.66416139]
----------------------------------------
Trial 481
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
n_estimators=97, random_state=100,
subsample=0.65))])
cv score: [0.7725 0.68333333 0.7575 0.85416667 0.89082278]
----------------------------------------
Trial 482
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=62,
random_state=100))])
cv score: [0.69 0.62416667 0.715 0.84583333 0.83623418]
----------------------------------------
Trial 483
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='sqrt',
n_estimators=153,
random_state=100))])
cv score: [0.69833333 0.59083333 0.70916667 0.775 0.76028481]
----------------------------------------
Trial 484
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=75,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.7275 0.78583333 0.8675 0.8710443 ]
----------------------------------------
Trial 485
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=10,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69583333 0.7775 0.71833333 0.85083333 0.81764241]
----------------------------------------
Trial 486
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=190, random_state=100))])
cv score: [0.705 0.60833333 0.73458333 0.82208333 0.79984177]
----------------------------------------
Trial 487
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=118, random_state=100,
subsample=0.95))])
cv score: [0.66583333 0.6025 0.7175 0.75333333 0.77294304]
----------------------------------------
Trial 488
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.73916667 0.78625 0.8575 0.88449367]
----------------------------------------
Trial 489
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=146,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6 0.63916667 0.72833333 0.72916667 0.73022152]
----------------------------------------
Trial 490
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=197, random_state=100,
subsample=0.8))])
cv score: [0.53 0.605 0.6875 0.735 0.77927215]
----------------------------------------
Trial 491
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.685 0.575 0.76083333 0.7225 0.71914557]
----------------------------------------
Trial 492
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
n_estimators=164, random_state=100,
subsample=0.6))])
cv score: [0.75916667 0.6725 0.79166667 0.83916667 0.82832278]
----------------------------------------
Trial 493
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=141,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73666667 0.72166667 0.77833333 0.83416667 0.90585443]
----------------------------------------
Trial 494
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79666667 0.72666667 0.78416667 0.84 0.82199367]
----------------------------------------
Trial 495
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=172, random_state=100,
subsample=0.8))])
cv score: [0.74541667 0.71416667 0.73583333 0.87416667 0.87539557]
----------------------------------------
Trial 496
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=143,
random_state=100, subsample=0.7))])
cv score: [0.71583333 0.63791667 0.7325 0.75333333 0.68987342]
----------------------------------------
Trial 497
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66 0.68166667 0.7775 0.75166667 0.83227848]
----------------------------------------
Trial 498
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='sqrt',
n_estimators=181, random_state=100,
subsample=0.75))])
cv score: [0.705 0.60916667 0.70666667 0.78666667 0.80221519]
----------------------------------------
Trial 499
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=130,
random_state=100))])
cv score: [0.69333333 0.5975 0.67416667 0.80583333 0.83860759]
----------------------------------------
Trial 500
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=91,
random_state=100))])
cv score: [0.55791667 0.66708333 0.69958333 0.82958333 0.80023734]
----------------------------------------
Trial 501
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.7075 0.79166667 0.82833333 0.89082278]
----------------------------------------
Trial 502
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=111,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70333333 0.6825 0.79416667 0.8275 0.84810127]
----------------------------------------
Trial 503
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=197, random_state=100))])
cv score: [0.685 0.63291667 0.72083333 0.79833333 0.83623418]
----------------------------------------
Trial 504
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=137, random_state=100,
subsample=0.8))])
cv score: [0.71166667 0.68083333 0.74666667 0.83166667 0.81962025]
----------------------------------------
Trial 505
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=170, random_state=100,
subsample=0.9))])
cv score: [0.5625 0.54666667 0.615 0.7025 0.71439873]
----------------------------------------
Trial 506
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71916667 0.74416667 0.765 0.85416667 0.87737342]
----------------------------------------
Trial 507
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=104, random_state=100))])
cv score: [0.73416667 0.68 0.78666667 0.83166667 0.84731013]
----------------------------------------
Trial 508
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71666667 0.69416667 0.78583333 0.8 0.84651899]
----------------------------------------
Trial 509
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=164,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.69166667 0.8125 0.83333333 0.88449367]
----------------------------------------
Trial 510
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73166667 0.69416667 0.80833333 0.83333333 0.8789557 ]
----------------------------------------
Trial 511
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=21, random_state=100))])
cv score: [0.66333333 0.71083333 0.68541667 0.79333333 0.9022943 ]
----------------------------------------
Trial 512
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=69, random_state=100,
subsample=0.85))])
cv score: [0.65083333 0.58166667 0.64166667 0.76083333 0.67484177]
----------------------------------------
Trial 513
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=112,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78166667 0.70875 0.77041667 0.85 0.87658228]
----------------------------------------
Trial 514
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, n_estimators=103,
random_state=100, subsample=0.6))])
cv score: [0.735 0.66 0.7275 0.74333333 0.7721519 ]
----------------------------------------
Trial 515
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=122,
random_state=100))])
cv score: [0.51291667 0.66625 0.70541667 0.81416667 0.76107595]
----------------------------------------
Trial 516
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.55833333 0.6175 0.7525 0.7125 0.83939873]
----------------------------------------
Trial 517
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='sqrt',
n_estimators=135, random_state=100,
subsample=0.95))])
cv score: [0.57333333 0.51166667 0.65583333 0.74333333 0.6835443 ]
----------------------------------------
Trial 518
----------------------------------------
Parameters {'rf__n_estimators': 25, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=25,
random_state=100))])
cv score: [0.62 0.65166667 0.67833333 0.83125 0.82990506]
----------------------------------------
Trial 519
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.72333333 0.77166667 0.85166667 0.86234177]
----------------------------------------
Trial 520
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=146,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5675 0.59083333 0.76083333 0.7225 0.75553797]
----------------------------------------
Trial 521
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=161, random_state=100,
subsample=0.75))])
cv score: [0.715 0.63 0.70666667 0.79416667 0.78085443]
----------------------------------------
Trial 522
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=54,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.70916667 0.79833333 0.84166667 0.90348101]
----------------------------------------
Trial 523
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=128, random_state=100))])
cv score: [0.7275 0.68333333 0.79083333 0.83416667 0.84810127]
----------------------------------------
Trial 524
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=42,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69 0.6075 0.735 0.76 0.80537975]
----------------------------------------
Trial 525
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=50, random_state=100))])
cv score: [0.65583333 0.62166667 0.74333333 0.82083333 0.78401899]
----------------------------------------
Trial 526
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=192,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61166667 0.61416667 0.73166667 0.755 0.74920886]
----------------------------------------
Trial 527
----------------------------------------
Parameters {'rf__n_estimators': 25, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=25, random_state=100))])
cv score: [0.61583333 0.66083333 0.71 0.72833333 0.83227848]
----------------------------------------
Trial 528
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=113,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76333333 0.70416667 0.76833333 0.815 0.90348101]
----------------------------------------
Trial 529
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=31,
random_state=100))])
cv score: [0.68416667 0.56458333 0.69666667 0.83083333 0.83781646]
----------------------------------------
Trial 530
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=10,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5525 0.56916667 0.64083333 0.76958333 0.73259494]
----------------------------------------
Trial 531
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=74, random_state=100))])
cv score: [0.7425 0.66625 0.78083333 0.84208333 0.83623418]
----------------------------------------
Trial 532
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.73083333 0.80916667 0.85083333 0.87420886]
----------------------------------------
Trial 533
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=103, random_state=100,
subsample=0.95))])
cv score: [0.66083333 0.5425 0.7 0.7825 0.73101266]
----------------------------------------
Trial 534
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7225 0.73291667 0.78 0.86666667 0.85838608]
----------------------------------------
Trial 535
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78708333 0.73541667 0.80708333 0.85916667 0.90664557]
----------------------------------------
Trial 536
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=96,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57666667 0.585 0.70416667 0.70416667 0.7278481 ]
----------------------------------------
Trial 537
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6075 0.67083333 0.75833333 0.71916667 0.68275316]
----------------------------------------
Trial 538
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=189, random_state=100))])
cv score: [0.67 0.65666667 0.72083333 0.80916667 0.85601266]
----------------------------------------
Trial 539
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=47, random_state=100,
subsample=0.8))])
cv score: [0.6725 0.7075 0.71416667 0.84166667 0.81487342]
----------------------------------------
Trial 540
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=14,
random_state=100,
subsample=0.85))])
cv score: [0.615 0.62208333 0.79583333 0.73083333 0.67167722]
----------------------------------------
Trial 541
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=95, random_state=100,
subsample=0.85))])
cv score: [0.68666667 0.585 0.73916667 0.80333333 0.76898734]
----------------------------------------
Trial 542
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=107, random_state=100,
subsample=0.75))])
cv score: [0.69333333 0.61916667 0.71416667 0.8175 0.78560127]
----------------------------------------
Trial 543
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=10, random_state=100))])
cv score: [0.70083333 0.63166667 0.6975 0.75833333 0.78045886]
----------------------------------------
Trial 544
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=163,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 545
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=158,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76416667 0.72666667 0.79083333 0.845 0.88528481]
----------------------------------------
Trial 546
----------------------------------------
Parameters {'xgb__n_estimators': 160, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=160,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.8125 0.725 0.80416667 0.82333333 0.83939873]
----------------------------------------
Trial 547
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='log2',
n_estimators=63, random_state=100,
subsample=0.9))])
cv score: [0.67 0.56416667 0.65333333 0.71916667 0.76186709]
----------------------------------------
Trial 548
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=83, random_state=100))])
cv score: [0.73125 0.66708333 0.78166667 0.83541667 0.83267405]
----------------------------------------
Trial 549
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.64833333 0.75833333 0.73416667 0.74762658]
----------------------------------------
Trial 550
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=159,
random_state=100, subsample=0.6))])
cv score: [0.65666667 0.64833333 0.76416667 0.81083333 0.79825949]
----------------------------------------
Trial 551
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=87, random_state=100,
subsample=0.7))])
cv score: [0.6025 0.6425 0.695 0.7175 0.79113924]
----------------------------------------
Trial 552
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=161,
random_state=100))])
cv score: [0.64666667 0.58 0.73208333 0.78041667 0.78876582]
----------------------------------------
Trial 553
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='sqrt',
n_estimators=16, random_state=100,
subsample=0.85))])
cv score: [0.6775 0.595 0.74166667 0.78916667 0.77056962]
----------------------------------------
Trial 554
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=146,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.66333333 0.78876582]
----------------------------------------
Trial 555
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=86,
random_state=100))])
cv score: [0.60666667 0.67875 0.69416667 0.815 0.82515823]
----------------------------------------
Trial 556
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=99,
random_state=100))])
cv score: [0.64 0.58916667 0.73625 0.77958333 0.78322785]
----------------------------------------
Trial 557
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76708333 0.75666667 0.76958333 0.85625 0.84018987]
----------------------------------------
Trial 558
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.695 0.7275 0.7675 0.77583333 0.77452532]
----------------------------------------
Trial 559
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=128, random_state=100))])
cv score: [0.6775 0.62833333 0.75791667 0.82916667 0.82753165]
----------------------------------------
Trial 560
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=125, random_state=100,
subsample=0.75))])
cv score: [0.43666667 0.69 0.57666667 0.48666667 0.70648734]
----------------------------------------
Trial 561
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=181,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65666667 0.64 0.7875 0.79083333 0.85759494]
----------------------------------------
Trial 562
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='log2',
n_estimators=191, random_state=100,
subsample=0.65))])
cv score: [0.57833333 0.6 0.68916667 0.75 0.79984177]
----------------------------------------
Trial 563
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71333333 0.67666667 0.78583333 0.80666667 0.86867089]
----------------------------------------
Trial 564
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='sqrt',
n_estimators=196, random_state=100,
subsample=0.65))])
cv score: [0.61333333 0.59916667 0.65416667 0.72 0.80696203]
----------------------------------------
Trial 565
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='log2',
n_estimators=139, random_state=100,
subsample=0.8))])
cv score: [0.42333333 0.68166667 0.70666667 0.5125 0.47626582]
----------------------------------------
Trial 566
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='log2',
n_estimators=113, random_state=100,
subsample=0.85))])
cv score: [0.64416667 0.65666667 0.68083333 0.8025 0.82990506]
----------------------------------------
Trial 567
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=174, random_state=100))])
cv score: [0.68583333 0.62583333 0.74083333 0.825 0.82318038]
----------------------------------------
Trial 568
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=126, random_state=100,
subsample=0.7))])
cv score: [0.41083333 0.60666667 0.65916667 0.56 0.74367089]
----------------------------------------
Trial 569
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6975 0.64666667 0.7575 0.73666667 0.82120253]
----------------------------------------
Trial 570
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=64, random_state=100,
subsample=0.75))])
cv score: [0.625 0.64166667 0.68666667 0.85583333 0.79193038]
----------------------------------------
Trial 571
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=92,
random_state=100))])
cv score: [0.70583333 0.63625 0.7125 0.81083333 0.8346519 ]
----------------------------------------
Trial 572
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=175,
random_state=100,
subsample=0.75))])
cv score: [0.715 0.6475 0.76916667 0.765 0.81487342]
----------------------------------------
Trial 573
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=198,
random_state=100))])
cv score: [0.69416667 0.51791667 0.7975 0.67083333 0.62776899]
----------------------------------------
Trial 574
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=169, random_state=100,
subsample=0.95))])
cv score: [0.775 0.66916667 0.69458333 0.85291667 0.82792722]
----------------------------------------
Trial 575
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57666667 0.58083333 0.74916667 0.78666667 0.75949367]
----------------------------------------
Trial 576
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.745 0.735 0.77 0.85583333 0.87262658]
----------------------------------------
Trial 577
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=80,
random_state=100, subsample=0.6))])
cv score: [0.65166667 0.7125 0.73583333 0.83083333 0.85522152]
----------------------------------------
Trial 578
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=133,
random_state=100))])
cv score: [0.68625 0.51375 0.82416667 0.66375 0.66416139]
----------------------------------------
Trial 579
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63125 0.71 0.74666667 0.82666667 0.84533228]
----------------------------------------
Trial 580
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=58, random_state=100,
subsample=0.75))])
cv score: [0.635 0.63708333 0.5425 0.50833333 0.63132911]
----------------------------------------
Trial 581
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
n_estimators=89, random_state=100,
subsample=0.6))])
cv score: [0.65916667 0.62416667 0.6725 0.79166667 0.73971519]
----------------------------------------
Trial 582
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64666667 0.63333333 0.6625 0.74833333 0.77373418]
----------------------------------------
Trial 583
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=61, random_state=100))])
cv score: [0.58666667 0.71541667 0.6825 0.79166667 0.82674051]
----------------------------------------
Trial 584
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=138,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.65166667 0.77666667 0.745 0.81487342]
----------------------------------------
Trial 585
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=14, random_state=100,
subsample=0.75))])
cv score: [0.555 0.46833333 0.60833333 0.5475 0.67246835]
----------------------------------------
Trial 586
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=96, random_state=100,
subsample=0.85))])
cv score: [0.605 0.5675 0.66666667 0.665 0.77768987]
----------------------------------------
Trial 587
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67083333 0.6725 0.76166667 0.7225 0.84651899]
----------------------------------------
Trial 588
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.95))])
cv score: [0.6825 0.58833333 0.72166667 0.805 0.81803797]
----------------------------------------
Trial 589
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=54,
random_state=100))])
cv score: [0.62916667 0.62916667 0.65333333 0.62583333 0.75474684]
----------------------------------------
Trial 590
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=124,
random_state=100))])
cv score: [0.56291667 0.65875 0.68833333 0.83208333 0.80023734]
----------------------------------------
Trial 591
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69541667 0.73916667 0.75541667 0.83458333 0.85522152]
----------------------------------------
Trial 592
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=185,
random_state=100))])
cv score: [0.68708333 0.49375 0.80958333 0.64666667 0.75316456]
----------------------------------------
Trial 593
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=170,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7025 0.74625 0.78708333 0.8575 0.8528481 ]
----------------------------------------
Trial 594
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=68,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72833333 0.66166667 0.7625 0.80666667 0.8568038 ]
----------------------------------------
Trial 595
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=12, random_state=100,
subsample=0.65))])
cv score: [0.73333333 0.7125 0.79791667 0.76583333 0.80379747]
----------------------------------------
Trial 596
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.705 0.68833333 0.7775 0.78333333 0.83623418]
----------------------------------------
Trial 597
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=38, random_state=100,
subsample=0.8))])
cv score: [0.7275 0.6225 0.68166667 0.79083333 0.78481013]
----------------------------------------
Trial 598
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=158,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.655 0.65333333 0.76833333 0.7425 0.8085443 ]
----------------------------------------
Trial 599
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=73,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 600
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=158,
random_state=100))])
cv score: [0.6925 0.4875 0.71916667 0.66833333 0.63607595]
----------------------------------------
Trial 601
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.68166667 0.79916667 0.80083333 0.88686709]
----------------------------------------
Trial 602
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.70583333 0.79166667 0.82083333 0.88291139]
----------------------------------------
Trial 603
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=114, random_state=100))])
cv score: [0.74416667 0.67958333 0.79 0.83083333 0.85205696]
----------------------------------------
Trial 604
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='sqrt',
n_estimators=19,
random_state=100))])
cv score: [0.6275 0.5725 0.66166667 0.76166667 0.81329114]
----------------------------------------
Trial 605
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=133, random_state=100,
subsample=0.85))])
cv score: [0.68333333 0.625 0.7125 0.82833333 0.83227848]
----------------------------------------
Trial 606
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=85,
random_state=100))])
cv score: [0.73 0.63916667 0.80333333 0.80166667 0.73813291]
----------------------------------------
Trial 607
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=132, random_state=100))])
cv score: [0.70291667 0.61375 0.76458333 0.81041667 0.79193038]
----------------------------------------
Trial 608
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=14, random_state=100,
subsample=0.95))])
cv score: [0.69666667 0.5875 0.7625 0.77 0.78401899]
----------------------------------------
Trial 609
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=162,
random_state=100))])
cv score: [0.69083333 0.635 0.68583333 0.79166667 0.82594937]
----------------------------------------
Trial 610
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6775 0.67166667 0.7975 0.78 0.87737342]
----------------------------------------
Trial 611
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65916667 0.63833333 0.79416667 0.765 0.80775316]
----------------------------------------
Trial 612
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7775 0.71458333 0.78 0.845 0.90189873]
----------------------------------------
Trial 613
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='sqrt',
n_estimators=173, random_state=100,
subsample=0.95))])
cv score: [0.69166667 0.5775 0.695 0.79083333 0.8568038 ]
----------------------------------------
Trial 614
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=133, random_state=100))])
cv score: [0.6925 0.625 0.72 0.80333333 0.84810127]
----------------------------------------
Trial 615
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=150, random_state=100,
subsample=0.9))])
cv score: [0.745 0.60833333 0.78583333 0.82833333 0.79193038]
----------------------------------------
Trial 616
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=133, random_state=100))])
cv score: [0.76666667 0.69583333 0.78833333 0.83416667 0.8528481 ]
----------------------------------------
Trial 617
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59333333 0.70833333 0.7775 0.675 0.71993671]
----------------------------------------
Trial 618
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.6975 0.8225 0.7 0.79905063]
----------------------------------------
Trial 619
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=162,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62166667 0.61583333 0.735 0.7475 0.74683544]
----------------------------------------
Trial 620
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
n_estimators=24, random_state=100,
subsample=0.9))])
cv score: [0.7275 0.65458333 0.82416667 0.79416667 0.84731013]
----------------------------------------
Trial 621
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=138, random_state=100))])
cv score: [0.69333333 0.62083333 0.7175 0.79916667 0.84889241]
----------------------------------------
Trial 622
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=48, random_state=100,
subsample=0.65))])
cv score: [0.6575 0.65333333 0.6825 0.76083333 0.80300633]
----------------------------------------
Trial 623
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=154,
random_state=100))])
cv score: [0.68583333 0.51375 0.82291667 0.66458333 0.66416139]
----------------------------------------
Trial 624
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=193,
random_state=100))])
cv score: [0.54041667 0.66458333 0.71541667 0.82333333 0.79272152]
----------------------------------------
Trial 625
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='log2',
n_estimators=191, random_state=100,
subsample=0.7))])
cv score: [0.62916667 0.66416667 0.6925 0.74833333 0.64794304]
----------------------------------------
Trial 626
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=111, random_state=100))])
cv score: [0.68708333 0.63333333 0.74458333 0.82375 0.81566456]
----------------------------------------
Trial 627
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=35, random_state=100))])
cv score: [0.69666667 0.575 0.74833333 0.81083333 0.82357595]
----------------------------------------
Trial 628
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=107, random_state=100))])
cv score: [0.70416667 0.60208333 0.73166667 0.82625 0.8085443 ]
----------------------------------------
Trial 629
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.8025 0.735 0.79375 0.8275 0.85363924]
----------------------------------------
Trial 630
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=169,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67583333 0.61666667 0.75833333 0.74083333 0.75712025]
----------------------------------------
Trial 631
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=177, random_state=100))])
cv score: [0.66166667 0.6575 0.70416667 0.82416667 0.85522152]
----------------------------------------
Trial 632
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=34,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59666667 0.595 0.67833333 0.77333333 0.77531646]
----------------------------------------
Trial 633
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=75,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58083333 0.635 0.79 0.7175 0.78718354]
----------------------------------------
Trial 634
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=175, random_state=100))])
cv score: [0.70791667 0.61708333 0.735 0.82375 0.80221519]
----------------------------------------
Trial 635
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6225 0.6 0.78166667 0.73666667 0.76898734]
----------------------------------------
Trial 636
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=68,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65166667 0.63333333 0.76916667 0.73833333 0.75712025]
----------------------------------------
Trial 637
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77166667 0.7325 0.78833333 0.85833333 0.88291139]
----------------------------------------
Trial 638
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=164, random_state=100,
subsample=0.8))])
cv score: [0.68666667 0.6575 0.70583333 0.82583333 0.82753165]
----------------------------------------
Trial 639
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72166667 0.665 0.79 0.7925 0.88132911]
----------------------------------------
Trial 640
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=45,
random_state=100))])
cv score: [0.68541667 0.49375 0.80833333 0.64625 0.75237342]
----------------------------------------
Trial 641
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='log2',
n_estimators=182, random_state=100,
subsample=0.7))])
cv score: [0.58666667 0.57916667 0.7 0.6725 0.70806962]
----------------------------------------
Trial 642
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='sqrt',
n_estimators=83, random_state=100,
subsample=0.95))])
cv score: [0.6475 0.60083333 0.72166667 0.75 0.77848101]
----------------------------------------
Trial 643
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78083333 0.73333333 0.79041667 0.83583333 0.86629747]
----------------------------------------
Trial 644
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68416667 0.71916667 0.76291667 0.84916667 0.86075949]
----------------------------------------
Trial 645
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=190, random_state=100,
subsample=0.7))])
cv score: [0.60166667 0.64166667 0.73333333 0.7925 0.71835443]
----------------------------------------
Trial 646
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=165,
random_state=100))])
cv score: [0.54375 0.67458333 0.71291667 0.82416667 0.78955696]
----------------------------------------
Trial 647
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=17, random_state=100,
subsample=0.85))])
cv score: [0.495 0.555 0.62 0.6525 0.73971519]
----------------------------------------
Trial 648
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
n_estimators=72, random_state=100,
subsample=0.75))])
cv score: [0.7025 0.6775 0.76 0.83 0.81566456]
----------------------------------------
Trial 649
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=143,
random_state=100))])
cv score: [0.68666667 0.63166667 0.71083333 0.80583333 0.82594937]
----------------------------------------
Trial 650
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=110,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.55833333 0.6475 0.76 0.74916667 0.80775316]
----------------------------------------
Trial 651
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=47, random_state=100))])
cv score: [0.615 0.68083333 0.70541667 0.80416667 0.84335443]
----------------------------------------
Trial 652
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76666667 0.72916667 0.78208333 0.8525 0.92325949]
----------------------------------------
Trial 653
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=158,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56916667 0.63833333 0.59791667 0.75791667 0.78916139]
----------------------------------------
Trial 654
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.7175 0.8 0.83916667 0.89003165]
----------------------------------------
Trial 655
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=185, random_state=100,
subsample=0.6))])
cv score: [0.70083333 0.66333333 0.75166667 0.85166667 0.80063291]
----------------------------------------
Trial 656
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=192,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64333333 0.68333333 0.715 0.73 0.72151899]
----------------------------------------
Trial 657
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=128,
random_state=100))])
cv score: [0.6875 0.62 0.6825 0.7975 0.81566456]
----------------------------------------
Trial 658
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=12,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65083333 0.74458333 0.73375 0.83875 0.79905063]
----------------------------------------
Trial 659
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=105, random_state=100,
subsample=0.8))])
cv score: [0.68916667 0.65083333 0.7325 0.80583333 0.78797468]
----------------------------------------
Trial 660
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=164, random_state=100,
subsample=0.75))])
cv score: [0.60333333 0.65916667 0.59166667 0.71 0.78322785]
----------------------------------------
Trial 661
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=164,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6325 0.64333333 0.72666667 0.69916667 0.75474684]
----------------------------------------
Trial 662
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6925 0.685 0.79166667 0.83 0.88528481]
----------------------------------------
Trial 663
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=92, random_state=100,
subsample=0.7))])
cv score: [0.5425 0.61083333 0.64916667 0.67833333 0.75712025]
----------------------------------------
Trial 664
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=73,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69291667 0.72208333 0.74958333 0.86833333 0.88212025]
----------------------------------------
Trial 665
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=60,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.7125 0.78833333 0.80666667 0.90110759]
----------------------------------------
Trial 666
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=107,
random_state=100))])
cv score: [0.66833333 0.50541667 0.7975 0.66125 0.78797468]
----------------------------------------
Trial 667
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
n_estimators=30, random_state=100,
subsample=0.9))])
cv score: [0.72166667 0.72833333 0.73166667 0.72916667 0.70886076]
----------------------------------------
Trial 668
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7 0.68 0.74083333 0.74916667 0.74920886]
----------------------------------------
Trial 669
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61833333 0.62333333 0.73583333 0.7125 0.80142405]
----------------------------------------
Trial 670
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=161,
random_state=100))])
cv score: [0.6925 0.48916667 0.77791667 0.67083333 0.63844937]
----------------------------------------
Trial 671
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='sqrt',
n_estimators=27, random_state=100,
subsample=0.65))])
cv score: [0.61416667 0.65083333 0.6675 0.76166667 0.79193038]
----------------------------------------
Trial 672
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=152,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73666667 0.68 0.78416667 0.83333333 0.8931962 ]
----------------------------------------
Trial 673
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='log2',
n_estimators=193, random_state=100,
subsample=0.6))])
cv score: [0.68 0.62333333 0.69333333 0.79666667 0.80696203]
----------------------------------------
Trial 674
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=193,
random_state=100))])
cv score: [0.71083333 0.60166667 0.67083333 0.80333333 0.84493671]
----------------------------------------
Trial 675
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=13, random_state=100,
subsample=0.6))])
cv score: [0.60708333 0.675 0.67666667 0.675 0.66693038]
----------------------------------------
Trial 676
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=59,
random_state=100))])
cv score: [0.70416667 0.61833333 0.74416667 0.78333333 0.72231013]
----------------------------------------
Trial 677
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=109,
random_state=100))])
cv score: [0.6825 0.61 0.72 0.81 0.82278481]
----------------------------------------
Trial 678
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69791667 0.7575 0.78375 0.85583333 0.85996835]
----------------------------------------
Trial 679
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=181,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77916667 0.72041667 0.77916667 0.84083333 0.875 ]
----------------------------------------
Trial 680
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=163, random_state=100,
subsample=0.85))])
cv score: [0.64583333 0.615 0.7125 0.77083333 0.83702532]
----------------------------------------
Trial 681
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=69,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63916667 0.60666667 0.7925 0.785 0.82594937]
----------------------------------------
Trial 682
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=131, random_state=100,
subsample=0.85))])
cv score: [0.65416667 0.6375 0.77416667 0.78416667 0.69936709]
----------------------------------------
Trial 683
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=197,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.625 0.59166667 0.75333333 0.69583333 0.77136076]
----------------------------------------
Trial 684
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=23,
random_state=100))])
cv score: [0.71666667 0.62458333 0.72333333 0.74083333 0.83386076]
----------------------------------------
Trial 685
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=94, random_state=100,
subsample=0.85))])
cv score: [0.61416667 0.58 0.70166667 0.78583333 0.68987342]
----------------------------------------
Trial 686
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=75,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66041667 0.71375 0.73208333 0.84791667 0.85047468]
----------------------------------------
Trial 687
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=123,
random_state=100))])
cv score: [0.67666667 0.58583333 0.6975 0.7925 0.79905063]
----------------------------------------
Trial 688
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=65, random_state=100))])
cv score: [0.7325 0.68375 0.78166667 0.825 0.85047468]
----------------------------------------
Trial 689
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=33, random_state=100,
subsample=0.6))])
cv score: [0.70583333 0.67083333 0.79666667 0.79083333 0.79193038]
----------------------------------------
Trial 690
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=167, random_state=100,
subsample=0.6))])
cv score: [0.30791667 0.52833333 0.42833333 0.62166667 0.39833861]
----------------------------------------
Trial 691
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=174,
random_state=100))])
cv score: [0.63791667 0.57583333 0.73291667 0.79 0.78955696]
----------------------------------------
Trial 692
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=75,
random_state=100))])
cv score: [0.62958333 0.58666667 0.73541667 0.75291667 0.78837025]
----------------------------------------
Trial 693
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=135, random_state=100,
subsample=0.65))])
cv score: [0.6625 0.65166667 0.81 0.68 0.64794304]
----------------------------------------
Trial 694
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=42,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74625 0.72666667 0.76625 0.845 0.86946203]
----------------------------------------
Trial 695
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62 0.61 0.75666667 0.695 0.76107595]
----------------------------------------
Trial 696
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70333333 0.65 0.74833333 0.6525 0.72072785]
----------------------------------------
Trial 697
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=141,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69 0.64083333 0.75833333 0.76166667 0.83544304]
----------------------------------------
Trial 698
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='sqrt',
n_estimators=87, random_state=100,
subsample=0.85))])
cv score: [0.71583333 0.64916667 0.7 0.80416667 0.78085443]
----------------------------------------
Trial 699
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=108, random_state=100))])
cv score: [0.68583333 0.64208333 0.73 0.81625 0.82911392]
----------------------------------------
Trial 700
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=16,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65666667 0.64125 0.75333333 0.6925 0.77136076]
----------------------------------------
Trial 701
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=58,
random_state=100))])
cv score: [0.67333333 0.58208333 0.7075 0.8075 0.83544304]
----------------------------------------
Trial 702
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=195,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.595 0.71125 0.73041667 0.85166667 0.82080696]
----------------------------------------
Trial 703
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=142, random_state=100))])
cv score: [0.69833333 0.63666667 0.73083333 0.81083333 0.83544304]
----------------------------------------
Trial 704
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=37, random_state=100,
subsample=0.75))])
cv score: [0.6825 0.7075 0.72583333 0.83333333 0.78322785]
----------------------------------------
Trial 705
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=58, random_state=100))])
cv score: [0.66833333 0.61583333 0.70583333 0.77166667 0.84335443]
----------------------------------------
Trial 706
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='sqrt',
n_estimators=56, random_state=100,
subsample=0.8))])
cv score: [0.67333333 0.65083333 0.6975 0.82 0.82357595]
----------------------------------------
Trial 707
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=10,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67333333 0.73875 0.785 0.80166667 0.84651899]
----------------------------------------
Trial 708
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='sqrt',
n_estimators=114, random_state=100,
subsample=0.95))])
cv score: [0.6625 0.62333333 0.69083333 0.805 0.85363924]
----------------------------------------
Trial 709
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=172, random_state=100,
subsample=0.75))])
cv score: [0.74833333 0.66083333 0.78916667 0.8375 0.82911392]
----------------------------------------
Trial 710
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=96,
random_state=100))])
cv score: [0.61166667 0.67041667 0.69666667 0.8075 0.82832278]
----------------------------------------
Trial 711
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62583333 0.6625 0.77166667 0.7575 0.8085443 ]
----------------------------------------
Trial 712
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=114, random_state=100,
subsample=0.65))])
cv score: [0.6925 0.63333333 0.73083333 0.79166667 0.80775316]
----------------------------------------
Trial 713
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=160, random_state=100))])
cv score: [0.69333333 0.655 0.71666667 0.79583333 0.8346519 ]
----------------------------------------
Trial 714
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, n_estimators=163,
random_state=100,
subsample=0.65))])
cv score: [0.68416667 0.665 0.7475 0.765 0.77927215]
----------------------------------------
Trial 715
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=162, random_state=100,
subsample=0.85))])
cv score: [0.63666667 0.65416667 0.73166667 0.78166667 0.79509494]
----------------------------------------
Trial 716
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=113,
random_state=100))])
cv score: [0.68166667 0.65333333 0.72583333 0.80083333 0.84493671]
----------------------------------------
Trial 717
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.69833333 0.78666667 0.81 0.8528481 ]
----------------------------------------
Trial 718
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=19, random_state=100))])
cv score: [0.73333333 0.67 0.7675 0.87708333 0.84493671]
----------------------------------------
Trial 719
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70208333 0.74833333 0.75583333 0.86166667 0.85205696]
----------------------------------------
Trial 720
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=180,
random_state=100))])
cv score: [0.75 0.58791667 0.80833333 0.7925 0.80458861]
----------------------------------------
Trial 721
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=188, random_state=100))])
cv score: [0.6825 0.63416667 0.725 0.79666667 0.83781646]
----------------------------------------
Trial 722
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='log2',
n_estimators=139, random_state=100,
subsample=0.9))])
cv score: [0.64833333 0.6525 0.73166667 0.83916667 0.83227848]
----------------------------------------
Trial 723
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=54,
random_state=100))])
cv score: [0.66833333 0.50541667 0.795 0.66375 0.78876582]
----------------------------------------
Trial 724
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=134,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72291667 0.70875 0.77208333 0.84416667 0.87183544]
----------------------------------------
Trial 725
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
n_estimators=195, random_state=100,
subsample=0.8))])
cv score: [0.69833333 0.6425 0.74916667 0.80166667 0.79272152]
----------------------------------------
Trial 726
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=17,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69333333 0.73541667 0.74875 0.86 0.84810127]
----------------------------------------
Trial 727
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='log2',
n_estimators=164, random_state=100,
subsample=0.95))])
cv score: [0.70166667 0.56416667 0.73083333 0.8 0.80221519]
----------------------------------------
Trial 728
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=115,
random_state=100))])
cv score: [0.6875 0.63166667 0.69083333 0.79583333 0.81012658]
----------------------------------------
Trial 729
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=17,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70958333 0.74 0.74333333 0.85458333 0.89003165]
----------------------------------------
Trial 730
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=161, random_state=100,
subsample=0.95))])
cv score: [0.6825 0.61916667 0.64833333 0.73916667 0.77452532]
----------------------------------------
Trial 731
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=147,
random_state=100))])
cv score: [0.64291667 0.57708333 0.74 0.78833333 0.7903481 ]
----------------------------------------
Trial 732
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=54, random_state=100,
subsample=0.85))])
cv score: [0.66833333 0.52666667 0.69166667 0.76 0.7681962 ]
----------------------------------------
Trial 733
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=57,
random_state=100))])
cv score: [0.64333333 0.66958333 0.6825 0.80666667 0.83227848]
----------------------------------------
Trial 734
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=35,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71083333 0.7275 0.77833333 0.84583333 0.88765823]
----------------------------------------
Trial 735
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=10,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.69333333 0.75666667 0.7825 0.76186709]
----------------------------------------
Trial 736
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.67666667 0.7925 0.79083333 0.8528481 ]
----------------------------------------
Trial 737
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=80,
random_state=100))])
cv score: [0.56 0.59333333 0.69 0.7175 0.54667722]
----------------------------------------
Trial 738
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=71,
random_state=100))])
cv score: [0.65916667 0.49666667 0.80541667 0.65625 0.81566456]
----------------------------------------
Trial 739
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='log2',
n_estimators=139,
random_state=100))])
cv score: [0.68916667 0.59583333 0.71 0.77333333 0.81170886]
----------------------------------------
Trial 740
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=163,
random_state=100))])
cv score: [0.6925 0.50125 0.72375 0.64875 0.63291139]
----------------------------------------
Trial 741
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='log2',
n_estimators=160, random_state=100,
subsample=0.7))])
cv score: [0.60916667 0.53833333 0.63416667 0.60083333 0.68908228]
----------------------------------------
Trial 742
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='sqrt',
n_estimators=198, random_state=100,
subsample=0.7))])
cv score: [0.66666667 0.61583333 0.73 0.79916667 0.80221519]
----------------------------------------
Trial 743
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
n_estimators=179, random_state=100,
subsample=0.9))])
cv score: [0.63083333 0.63166667 0.72833333 0.7925 0.73655063]
----------------------------------------
Trial 744
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=69, random_state=100))])
cv score: [0.63916667 0.66166667 0.74166667 0.7975 0.82278481]
----------------------------------------
Trial 745
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=75, random_state=100,
subsample=0.9))])
cv score: [0.665 0.69708333 0.70791667 0.83333333 0.83860759]
----------------------------------------
Trial 746
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=154, random_state=100))])
cv score: [0.75416667 0.67833333 0.78416667 0.83583333 0.85205696]
----------------------------------------
Trial 747
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73166667 0.67166667 0.79 0.78333333 0.82832278]
----------------------------------------
Trial 748
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
n_estimators=36, random_state=100,
subsample=0.75))])
cv score: [0.58625 0.6825 0.61958333 0.84625 0.8164557 ]
----------------------------------------
Trial 749
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=55, random_state=100))])
cv score: [0.66625 0.60708333 0.73291667 0.8175 0.77531646]
----------------------------------------
Trial 750
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=138,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.67166667 0.7775 0.80416667 0.86708861]
----------------------------------------
Trial 751
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=64,
random_state=100))])
cv score: [0.61625 0.59083333 0.73291667 0.74458333 0.81012658]
----------------------------------------
Trial 752
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6575 0.69083333 0.75833333 0.77 0.85047468]
----------------------------------------
Trial 753
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, n_estimators=74,
random_state=100, subsample=0.8))])
cv score: [0.72 0.65916667 0.71916667 0.81833333 0.82357595]
----------------------------------------
Trial 754
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.785 0.73666667 0.78833333 0.86833333 0.86550633]
----------------------------------------
Trial 755
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=96,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73833333 0.70333333 0.79583333 0.84916667 0.90348101]
----------------------------------------
Trial 756
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.6))])
cv score: [0.62583333 0.61666667 0.64916667 0.84083333 0.78639241]
----------------------------------------
Trial 757
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77333333 0.75375 0.79333333 0.8675 0.85838608]
----------------------------------------
Trial 758
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.7))])
cv score: [0.69083333 0.61333333 0.69083333 0.78916667 0.8346519 ]
----------------------------------------
Trial 759
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=38, random_state=100))])
cv score: [0.68583333 0.67583333 0.6825 0.80916667 0.86787975]
----------------------------------------
Trial 760
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73333333 0.73625 0.7725 0.8425 0.87737342]
----------------------------------------
Trial 761
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.785 0.71208333 0.77833333 0.855 0.91851266]
----------------------------------------
Trial 762
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=149,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 763
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=164, random_state=100))])
cv score: [0.7025 0.63916667 0.73083333 0.80708333 0.82594937]
----------------------------------------
Trial 764
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=96,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72916667 0.73375 0.77291667 0.85833333 0.87183544]
----------------------------------------
Trial 765
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=42,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66375 0.75083333 0.77541667 0.85166667 0.8710443 ]
----------------------------------------
Trial 766
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=77, random_state=100))])
cv score: [0.655 0.63 0.7575 0.80666667 0.82199367]
----------------------------------------
Trial 767
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=128,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 768
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='sqrt',
n_estimators=82, random_state=100,
subsample=0.95))])
cv score: [0.65916667 0.62333333 0.73333333 0.7 0.78797468]
----------------------------------------
Trial 769
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=173,
random_state=100))])
cv score: [0.6775 0.57916667 0.72333333 0.82 0.83148734]
----------------------------------------
Trial 770
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=123,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 771
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=42, random_state=100,
subsample=0.85))])
cv score: [0.6375 0.6075 0.71166667 0.79416667 0.78797468]
----------------------------------------
Trial 772
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=188, random_state=100,
subsample=0.9))])
cv score: [0.56333333 0.5475 0.70333333 0.54416667 0.7056962 ]
----------------------------------------
Trial 773
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='log2',
n_estimators=96, random_state=100,
subsample=0.65))])
cv score: [0.67583333 0.6575 0.715 0.80583333 0.82278481]
----------------------------------------
Trial 774
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
n_estimators=116, random_state=100,
subsample=0.85))])
cv score: [0.71666667 0.64 0.80666667 0.82166667 0.80537975]
----------------------------------------
Trial 775
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=64, random_state=100,
subsample=0.85))])
cv score: [0.66166667 0.60833333 0.7 0.7725 0.77610759]
----------------------------------------
Trial 776
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='sqrt',
n_estimators=198, random_state=100,
subsample=0.75))])
cv score: [0.50333333 0.61333333 0.70416667 0.68583333 0.75 ]
----------------------------------------
Trial 777
----------------------------------------
Parameters {'rf__n_estimators': 60, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=60,
random_state=100))])
cv score: [0.665 0.50541667 0.795 0.66458333 0.78876582]
----------------------------------------
Trial 778
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=86,
random_state=100))])
cv score: [0.63666667 0.595 0.73625 0.77125 0.77689873]
----------------------------------------
Trial 779
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=137,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71666667 0.7225 0.74833333 0.86 0.87025316]
----------------------------------------
Trial 780
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=126, random_state=100,
subsample=0.95))])
cv score: [0.7275 0.63541667 0.80416667 0.825 0.76186709]
----------------------------------------
Trial 781
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=137,
random_state=100))])
cv score: [0.55791667 0.67041667 0.69416667 0.82708333 0.80458861]
----------------------------------------
Trial 782
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=67, random_state=100))])
cv score: [0.61333333 0.68416667 0.63416667 0.81833333 0.8164557 ]
----------------------------------------
Trial 783
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=141,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.67416667 0.76833333 0.78666667 0.85601266]
----------------------------------------
Trial 784
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=49,
random_state=100))])
cv score: [0.57458333 0.66625 0.70458333 0.82208333 0.82832278]
----------------------------------------
Trial 785
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=166, random_state=100,
subsample=0.7))])
cv score: [0.64833333 0.64083333 0.69166667 0.81166667 0.83544304]
----------------------------------------
Trial 786
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=171, random_state=100,
subsample=0.65))])
cv score: [0.645 0.57083333 0.69083333 0.69583333 0.73101266]
----------------------------------------
Trial 787
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=62, random_state=100))])
cv score: [0.68083333 0.61625 0.7325 0.81458333 0.79667722]
----------------------------------------
Trial 788
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='log2',
n_estimators=59, random_state=100,
subsample=0.8))])
cv score: [0.66583333 0.65083333 0.69083333 0.63083333 0.63686709]
----------------------------------------
Trial 789
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=27,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79083333 0.71791667 0.795 0.84083333 0.88528481]
----------------------------------------
Trial 790
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.65))])
cv score: [0.65166667 0.615 0.71916667 0.82416667 0.79351266]
----------------------------------------
Trial 791
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.67833333 0.7825 0.81666667 0.8931962 ]
----------------------------------------
Trial 792
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=164,
random_state=100))])
cv score: [0.68833333 0.5925 0.70166667 0.77 0.76740506]
----------------------------------------
Trial 793
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=104, random_state=100))])
cv score: [0.68916667 0.66333333 0.72583333 0.795 0.85838608]
----------------------------------------
Trial 794
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=14, random_state=100,
subsample=0.95))])
cv score: [0.64291667 0.62833333 0.72916667 0.76833333 0.75553797]
----------------------------------------
Trial 795
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=82, random_state=100,
subsample=0.75))])
cv score: [0.71166667 0.63416667 0.69666667 0.81583333 0.81170886]
----------------------------------------
Trial 796
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73083333 0.68583333 0.7775 0.7675 0.85601266]
----------------------------------------
Trial 797
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=187,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 798
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71166667 0.70166667 0.79583333 0.81166667 0.84572785]
----------------------------------------
Trial 799
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=74, random_state=100,
subsample=0.6))])
cv score: [0.675 0.63916667 0.66916667 0.74416667 0.82990506]
----------------------------------------
Trial 800
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='log2',
n_estimators=176, random_state=100,
subsample=0.75))])
cv score: [0.46541667 0.5825 0.57166667 0.55 0.53164557]
----------------------------------------
Trial 801
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57833333 0.68291667 0.65791667 0.77041667 0.69185127]
----------------------------------------
Trial 802
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=143, random_state=100))])
cv score: [0.70458333 0.61291667 0.75375 0.81083333 0.7903481 ]
----------------------------------------
Trial 803
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60416667 0.6375 0.7475 0.76916667 0.80300633]
----------------------------------------
Trial 804
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='log2',
n_estimators=146, random_state=100,
subsample=0.8))])
cv score: [0.54666667 0.57833333 0.5925 0.6325 0.77610759]
----------------------------------------
Trial 805
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76208333 0.71416667 0.78458333 0.82625 0.8477057 ]
----------------------------------------
Trial 806
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=147,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78041667 0.7325 0.76166667 0.85333333 0.87341772]
----------------------------------------
Trial 807
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=81,
random_state=100))])
cv score: [0.67208333 0.70333333 0.74375 0.82791667 0.84651899]
----------------------------------------
Trial 808
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=180,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6225 0.595 0.74833333 0.67916667 0.73971519]
----------------------------------------
Trial 809
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=179,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.765 0.71666667 0.795 0.85333333 0.88844937]
----------------------------------------
Trial 810
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=24, random_state=100))])
cv score: [0.635 0.64166667 0.71166667 0.73916667 0.84098101]
----------------------------------------
Trial 811
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=111, random_state=100))])
cv score: [0.7275 0.68458333 0.7925 0.83333333 0.84572785]
----------------------------------------
Trial 812
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='sqrt',
n_estimators=17, random_state=100,
subsample=0.8))])
cv score: [0.61666667 0.6875 0.66166667 0.73 0.81724684]
----------------------------------------
Trial 813
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=45, random_state=100))])
cv score: [0.65583333 0.67833333 0.7 0.78416667 0.83781646]
----------------------------------------
Trial 814
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=60,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7375 0.715 0.81 0.79416667 0.85601266]
----------------------------------------
Trial 815
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=163,
random_state=100,
subsample=0.95))])
cv score: [0.7275 0.70833333 0.71333333 0.79083333 0.80221519]
----------------------------------------
Trial 816
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=34,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65833333 0.585 0.79 0.73583333 0.7681962 ]
----------------------------------------
Trial 817
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=36, random_state=100))])
cv score: [0.6925 0.66916667 0.68333333 0.81083333 0.87183544]
----------------------------------------
Trial 818
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=92, random_state=100))])
cv score: [0.68416667 0.6525 0.73083333 0.79416667 0.84256329]
----------------------------------------
Trial 819
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=184,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78583333 0.71791667 0.80333333 0.835 0.84177215]
----------------------------------------
Trial 820
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=198,
random_state=100))])
cv score: [0.54375 0.66625 0.71458333 0.82333333 0.79272152]
----------------------------------------
Trial 821
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76833333 0.71166667 0.75833333 0.80083333 0.89161392]
----------------------------------------
Trial 822
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=78, random_state=100,
subsample=0.95))])
cv score: [0.64916667 0.55333333 0.70583333 0.77666667 0.7318038 ]
----------------------------------------
Trial 823
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=176, random_state=100,
subsample=0.85))])
cv score: [0.66083333 0.57083333 0.69416667 0.75166667 0.77610759]
----------------------------------------
Trial 824
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=76, random_state=100))])
cv score: [0.7 0.63333333 0.73291667 0.81541667 0.78599684]
----------------------------------------
Trial 825
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
n_estimators=159, random_state=100,
subsample=0.8))])
cv score: [0.77166667 0.6525 0.77916667 0.83583333 0.86234177]
----------------------------------------
Trial 826
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
n_estimators=80, random_state=100,
subsample=0.8))])
cv score: [0.72083333 0.6725 0.80083333 0.84666667 0.81012658]
----------------------------------------
Trial 827
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=111, random_state=100))])
cv score: [0.65166667 0.6775 0.75416667 0.7825 0.84414557]
----------------------------------------
Trial 828
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=38, random_state=100,
subsample=0.6))])
cv score: [0.71166667 0.64833333 0.6825 0.76666667 0.80458861]
----------------------------------------
Trial 829
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58333333 0.60333333 0.76666667 0.74166667 0.73575949]
----------------------------------------
Trial 830
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=191,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68833333 0.755 0.7775 0.8725 0.86313291]
----------------------------------------
Trial 831
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=49, random_state=100,
subsample=0.7))])
cv score: [0.70166667 0.64416667 0.71833333 0.81083333 0.84731013]
----------------------------------------
Trial 832
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77083333 0.7225 0.78666667 0.84833333 0.88132911]
----------------------------------------
Trial 833
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61666667 0.64083333 0.7825 0.73583333 0.79509494]
----------------------------------------
Trial 834
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=128,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.745 0.6975 0.80916667 0.8425 0.875 ]
----------------------------------------
Trial 835
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66583333 0.655 0.7525 0.74333333 0.77531646]
----------------------------------------
Trial 836
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71541667 0.73625 0.76958333 0.86291667 0.86669304]
----------------------------------------
Trial 837
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=65,
random_state=100))])
cv score: [0.6975 0.63708333 0.70833333 0.78666667 0.84256329]
----------------------------------------
Trial 838
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=65, random_state=100,
subsample=0.85))])
cv score: [0.69833333 0.58916667 0.71666667 0.80916667 0.81408228]
----------------------------------------
Trial 839
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=26, random_state=100,
subsample=0.95))])
cv score: [0.59041667 0.63583333 0.70375 0.78291667 0.77848101]
----------------------------------------
Trial 840
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='sqrt',
n_estimators=198, random_state=100,
subsample=0.9))])
cv score: [0.61666667 0.6275 0.70916667 0.77166667 0.77373418]
----------------------------------------
Trial 841
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=21,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75 0.72958333 0.77333333 0.8425 0.86234177]
----------------------------------------
Trial 842
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=29,
random_state=100))])
cv score: [0.53083333 0.67833333 0.70833333 0.83833333 0.81131329]
----------------------------------------
Trial 843
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=20, random_state=100))])
cv score: [0.74083333 0.66291667 0.7825 0.85708333 0.83821203]
----------------------------------------
Trial 844
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=176, random_state=100,
subsample=0.7))])
cv score: [0.745 0.66083333 0.79 0.835 0.83860759]
----------------------------------------
Trial 845
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=181,
random_state=100))])
cv score: [0.62583333 0.67 0.70083333 0.81666667 0.82674051]
----------------------------------------
Trial 846
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=80,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 847
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=68,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7475 0.70166667 0.76833333 0.815 0.86313291]
----------------------------------------
Trial 848
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=20,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83833333 0.82199367]
----------------------------------------
Trial 849
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=69,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77166667 0.72833333 0.81416667 0.8475 0.86075949]
----------------------------------------
Trial 850
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=42,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.72458333 0.75583333 0.86 0.87262658]
----------------------------------------
Trial 851
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72 0.68083333 0.7925 0.7975 0.86787975]
----------------------------------------
Trial 852
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=12, random_state=100))])
cv score: [0.6925 0.64833333 0.68916667 0.77708333 0.86115506]
----------------------------------------
Trial 853
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=158, random_state=100))])
cv score: [0.73916667 0.67625 0.7825 0.83583333 0.84572785]
----------------------------------------
Trial 854
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=54,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70333333 0.75625 0.78958333 0.86083333 0.82753165]
----------------------------------------
Trial 855
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68416667 0.66083333 0.7075 0.78416667 0.81566456]
----------------------------------------
Trial 856
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61166667 0.6525 0.74333333 0.76833333 0.74683544]
----------------------------------------
Trial 857
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=111,
random_state=100))])
cv score: [0.68666667 0.59333333 0.70416667 0.78166667 0.80063291]
----------------------------------------
Trial 858
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77166667 0.7475 0.77791667 0.8375 0.8789557 ]
----------------------------------------
Trial 859
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=27, random_state=100))])
cv score: [0.66708333 0.64041667 0.70416667 0.80041667 0.82199367]
----------------------------------------
Trial 860
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71125 0.73375 0.76416667 0.845 0.85996835]
----------------------------------------
Trial 861
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=24,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.8375 0.74208861]
----------------------------------------
Trial 862
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=78,
random_state=100))])
cv score: [0.69208333 0.48916667 0.77791667 0.67125 0.63765823]
----------------------------------------
Trial 863
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=111, random_state=100,
subsample=0.75))])
cv score: [0.6425 0.59083333 0.71333333 0.82166667 0.82832278]
----------------------------------------
Trial 864
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=164,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7275 0.71666667 0.77583333 0.835 0.91297468]
----------------------------------------
Trial 865
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=164,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60583333 0.69666667 0.68166667 0.64166667 0.72863924]
----------------------------------------
Trial 866
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6575 0.66166667 0.71 0.8175 0.83702532]
----------------------------------------
Trial 867
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.6125 0.73666667 0.73 0.79746835]
----------------------------------------
Trial 868
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=194, random_state=100))])
cv score: [0.7625 0.6875 0.7825 0.84333333 0.84731013]
----------------------------------------
Trial 869
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=146,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.66333333 0.78876582]
----------------------------------------
Trial 870
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=140, random_state=100))])
cv score: [0.68833333 0.62666667 0.73666667 0.79583333 0.82674051]
----------------------------------------
Trial 871
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=143, random_state=100,
subsample=0.65))])
cv score: [0.72916667 0.6625 0.78666667 0.8275 0.8164557 ]
----------------------------------------
Trial 872
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=182, random_state=100,
subsample=0.7))])
cv score: [0.61916667 0.64833333 0.735 0.79416667 0.70648734]
----------------------------------------
Trial 873
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=79, random_state=100))])
cv score: [0.72208333 0.67291667 0.775 0.82833333 0.84256329]
----------------------------------------
Trial 874
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=17, random_state=100))])
cv score: [0.76041667 0.67791667 0.71916667 0.85875 0.86906646]
----------------------------------------
Trial 875
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=174, random_state=100,
subsample=0.65))])
cv score: [0.72083333 0.65416667 0.77 0.82333333 0.81962025]
----------------------------------------
Trial 876
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='sqrt',
n_estimators=162, random_state=100,
subsample=0.65))])
cv score: [0.65666667 0.54 0.66166667 0.73333333 0.72547468]
----------------------------------------
Trial 877
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=191,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60833333 0.65083333 0.78833333 0.76916667 0.84098101]
----------------------------------------
Trial 878
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=52,
random_state=100))])
cv score: [0.75416667 0.58791667 0.80875 0.79416667 0.80379747]
----------------------------------------
Trial 879
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=153, random_state=100))])
cv score: [0.695 0.63416667 0.74333333 0.79083333 0.82436709]
----------------------------------------
Trial 880
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=113,
random_state=100))])
cv score: [0.69333333 0.48916667 0.77916667 0.67083333 0.63844937]
----------------------------------------
Trial 881
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=29, random_state=100,
subsample=0.8))])
cv score: [0.68833333 0.67958333 0.7075 0.87916667 0.82357595]
----------------------------------------
Trial 882
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=20, random_state=100,
subsample=0.6))])
cv score: [0.69833333 0.69291667 0.75083333 0.87041667 0.86392405]
----------------------------------------
Trial 883
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58 0.72875 0.70666667 0.79416667 0.78995253]
----------------------------------------
Trial 884
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=71, random_state=100,
subsample=0.95))])
cv score: [0.65125 0.675 0.69041667 0.80125 0.83939873]
----------------------------------------
Trial 885
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=33, random_state=100,
subsample=0.85))])
cv score: [0.54916667 0.575 0.61666667 0.76 0.77610759]
----------------------------------------
Trial 886
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7725 0.75333333 0.8 0.85 0.8568038 ]
----------------------------------------
Trial 887
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=161, random_state=100))])
cv score: [0.72541667 0.67583333 0.77791667 0.83541667 0.83386076]
----------------------------------------
Trial 888
----------------------------------------
Parameters {'xgb__n_estimators': 56, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=56,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.665 0.74125 0.76375 0.85375 0.86115506]
----------------------------------------
Trial 889
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=183, random_state=100))])
cv score: [0.68666667 0.63416667 0.71833333 0.8 0.83386076]
----------------------------------------
Trial 890
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73583333 0.72833333 0.76833333 0.85833333 0.88053797]
----------------------------------------
Trial 891
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
n_estimators=76, random_state=100,
subsample=0.85))])
cv score: [0.75166667 0.62 0.80166667 0.81 0.82753165]
----------------------------------------
Trial 892
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=137,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77291667 0.72791667 0.77791667 0.835 0.85126582]
----------------------------------------
Trial 893
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65083333 0.69166667 0.78416667 0.825 0.83386076]
----------------------------------------
Trial 894
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.655 0.78333333 0.7775 0.83939873]
----------------------------------------
Trial 895
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61541667 0.69875 0.7325 0.84083333 0.80617089]
----------------------------------------
Trial 896
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6875 0.6375 0.7475 0.7275 0.81882911]
----------------------------------------
Trial 897
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=195,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61125 0.66125 0.68083333 0.81041667 0.78481013]
----------------------------------------
Trial 898
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=73,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.68416667 0.77666667 0.7575 0.84731013]
----------------------------------------
Trial 899
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=126,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77083333 0.6975 0.77833333 0.81416667 0.90981013]
----------------------------------------
Trial 900
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=96,
random_state=100))])
cv score: [0.6525 0.66666667 0.7 0.80666667 0.83939873]
----------------------------------------
Trial 901
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=39, random_state=100,
subsample=0.7))])
cv score: [0.59416667 0.57833333 0.74916667 0.71 0.77452532]
----------------------------------------
Trial 902
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=18, random_state=100,
subsample=0.9))])
cv score: [0.52666667 0.63708333 0.715 0.80541667 0.78085443]
----------------------------------------
Trial 903
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=111,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.67833333 0.7875 0.7975 0.87420886]
----------------------------------------
Trial 904
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74083333 0.69333333 0.80583333 0.84416667 0.87816456]
----------------------------------------
Trial 905
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=28, random_state=100,
subsample=0.6))])
cv score: [0.73 0.66666667 0.78833333 0.82583333 0.81566456]
----------------------------------------
Trial 906
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=148,
random_state=100))])
cv score: [0.56458333 0.66625 0.69 0.82458333 0.80933544]
----------------------------------------
Trial 907
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='log2',
n_estimators=171,
random_state=100))])
cv score: [0.53416667 0.5375 0.72 0.68833333 0.78085443]
----------------------------------------
Trial 908
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='log2',
n_estimators=178, random_state=100,
subsample=0.6))])
cv score: [0.59333333 0.63666667 0.61583333 0.71 0.7096519 ]
----------------------------------------
Trial 909
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='log2',
n_estimators=115,
random_state=100))])
cv score: [0.64916667 0.59166667 0.70583333 0.74666667 0.69936709]
----------------------------------------
Trial 910
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=147,
random_state=100, subsample=0.8))])
cv score: [0.71833333 0.67166667 0.76916667 0.7925 0.78560127]
----------------------------------------
Trial 911
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=87, random_state=100))])
cv score: [0.68083333 0.64208333 0.7375 0.8075 0.82318038]
----------------------------------------
Trial 912
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68208333 0.7575 0.73583333 0.85625 0.81447785]
----------------------------------------
Trial 913
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71333333 0.74583333 0.74333333 0.8575 0.84968354]
----------------------------------------
Trial 914
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=37, random_state=100))])
cv score: [0.63291667 0.64375 0.75541667 0.83666667 0.78995253]
----------------------------------------
Trial 915
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.715 0.79333333 0.83583333 0.86471519]
----------------------------------------
Trial 916
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='sqrt',
n_estimators=186, random_state=100,
subsample=0.75))])
cv score: [0.72083333 0.58916667 0.70083333 0.75 0.79984177]
----------------------------------------
Trial 917
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6825 0.665 0.73416667 0.77666667 0.83306962]
----------------------------------------
Trial 918
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=162, random_state=100))])
cv score: [0.69625 0.61583333 0.74333333 0.82208333 0.79707278]
----------------------------------------
Trial 919
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68166667 0.67583333 0.80166667 0.78333333 0.84731013]
----------------------------------------
Trial 920
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.74125 0.77833333 0.87416667 0.85443038]
----------------------------------------
Trial 921
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=159,
random_state=100))])
cv score: [0.57125 0.65791667 0.69583333 0.82375 0.81170886]
----------------------------------------
Trial 922
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=16,
random_state=100))])
cv score: [0.68916667 0.50125 0.72166667 0.64958333 0.63686709]
----------------------------------------
Trial 923
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=42, random_state=100,
subsample=0.8))])
cv score: [0.72416667 0.62416667 0.68333333 0.7925 0.78401899]
----------------------------------------
Trial 924
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=101,
random_state=100))])
cv score: [0.58083333 0.66166667 0.69333333 0.83333333 0.84098101]
----------------------------------------
Trial 925
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.69416667 0.79666667 0.78833333 0.85443038]
----------------------------------------
Trial 926
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=147, random_state=100,
subsample=0.6))])
cv score: [0.6625 0.4675 0.56916667 0.54416667 0.71993671]
----------------------------------------
Trial 927
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=108,
random_state=100))])
cv score: [0.56041667 0.66125 0.69 0.83041667 0.79469937]
----------------------------------------
Trial 928
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=122,
random_state=100))])
cv score: [0.66166667 0.5875 0.705 0.80958333 0.81724684]
----------------------------------------
Trial 929
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=189,
random_state=100))])
cv score: [0.585 0.67416667 0.70333333 0.82666667 0.83544304]
----------------------------------------
Trial 930
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78333333 0.72416667 0.78666667 0.84583333 0.92088608]
----------------------------------------
Trial 931
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=183,
random_state=100))])
cv score: [0.68708333 0.51375 0.82375 0.66416667 0.66416139]
----------------------------------------
Trial 932
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=97,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 933
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=95,
random_state=100))])
cv score: [0.75 0.59666667 0.79875 0.80208333 0.75949367]
----------------------------------------
Trial 934
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
n_estimators=156, random_state=100,
subsample=0.75))])
cv score: [0.70583333 0.65666667 0.75416667 0.81 0.83781646]
----------------------------------------
Trial 935
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.72916667 0.77625 0.85583333 0.8710443 ]
----------------------------------------
Trial 936
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59916667 0.61583333 0.74333333 0.71583333 0.75791139]
----------------------------------------
Trial 937
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=11,
random_state=100))])
cv score: [0.70041667 0.51125 0.7425 0.67625 0.63726266]
----------------------------------------
Trial 938
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=115,
random_state=100))])
cv score: [0.56791667 0.65458333 0.69166667 0.83208333 0.80181962]
----------------------------------------
Trial 939
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
n_estimators=128, random_state=100,
subsample=0.95))])
cv score: [0.75916667 0.6125 0.82 0.84333333 0.81012658]
----------------------------------------
Trial 940
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7525 0.71333333 0.79166667 0.82416667 0.86787975]
----------------------------------------
Trial 941
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75666667 0.72666667 0.79583333 0.83666667 0.89161392]
----------------------------------------
Trial 942
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=73,
random_state=100))])
cv score: [0.62416667 0.62666667 0.6825 0.6925 0.74287975]
----------------------------------------
Trial 943
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76 0.72833333 0.7825 0.86083333 0.87579114]
----------------------------------------
Trial 944
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=13, random_state=100))])
cv score: [0.70333333 0.62333333 0.64375 0.72958333 0.74960443]
----------------------------------------
Trial 945
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=151, random_state=100))])
cv score: [0.77583333 0.69916667 0.77583333 0.85166667 0.87341772]
----------------------------------------
Trial 946
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='log2',
n_estimators=141, random_state=100,
subsample=0.8))])
cv score: [0.63583333 0.58833333 0.70166667 0.74666667 0.78876582]
----------------------------------------
Trial 947
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=71,
random_state=100))])
cv score: [0.55625 0.67875 0.70875 0.82208333 0.80023734]
----------------------------------------
Trial 948
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=78,
random_state=100))])
cv score: [0.56916667 0.67083333 0.68666667 0.82791667 0.83386076]
----------------------------------------
Trial 949
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=133,
random_state=100))])
cv score: [0.62583333 0.67666667 0.7025 0.81583333 0.82911392]
----------------------------------------
Trial 950
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=112,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.66333333 0.78166667 0.79833333 0.86075949]
----------------------------------------
Trial 951
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='sqrt',
n_estimators=150, random_state=100,
subsample=0.75))])
cv score: [0.43666667 0.69 0.57666667 0.48666667 0.6914557 ]
----------------------------------------
Trial 952
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=80, random_state=100))])
cv score: [0.56416667 0.73125 0.69583333 0.78583333 0.79905063]
----------------------------------------
Trial 953
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=138, random_state=100))])
cv score: [0.67833333 0.645 0.72583333 0.81333333 0.83860759]
----------------------------------------
Trial 954
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67791667 0.71625 0.73458333 0.85333333 0.86234177]
----------------------------------------
Trial 955
----------------------------------------
Parameters {'rf__n_estimators': 25, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=25, random_state=100))])
cv score: [0.70791667 0.67791667 0.76333333 0.85541667 0.83702532]
----------------------------------------
Trial 956
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76916667 0.7325 0.78166667 0.84083333 0.89794304]
----------------------------------------
Trial 957
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=141,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58166667 0.62916667 0.77083333 0.71666667 0.81487342]
----------------------------------------
Trial 958
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=133, random_state=100,
subsample=0.7))])
cv score: [0.8075 0.67833333 0.52625 0.52916667 0.38132911]
----------------------------------------
Trial 959
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='log2',
n_estimators=36, random_state=100,
subsample=0.7))])
cv score: [0.67833333 0.60666667 0.68666667 0.77416667 0.82753165]
----------------------------------------
Trial 960
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='log2',
n_estimators=73, random_state=100,
subsample=0.8))])
cv score: [0.485 0.61166667 0.66916667 0.6325 0.77373418]
----------------------------------------
Trial 961
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='sqrt',
n_estimators=170, random_state=100,
subsample=0.75))])
cv score: [0.5975 0.59666667 0.65583333 0.73 0.75553797]
----------------------------------------
Trial 962
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=73, random_state=100))])
cv score: [0.69583333 0.6675 0.7275 0.7925 0.86867089]
----------------------------------------
Trial 963
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='log2',
n_estimators=57, random_state=100,
subsample=0.9))])
cv score: [0.6575 0.62416667 0.72583333 0.79916667 0.82594937]
----------------------------------------
Trial 964
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=13,
random_state=100,
subsample=0.85))])
cv score: [0.74 0.69708333 0.72625 0.85291667 0.84691456]
----------------------------------------
Trial 965
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='sqrt',
n_estimators=104, random_state=100,
subsample=0.85))])
cv score: [0.62 0.63583333 0.675 0.71333333 0.69066456]
----------------------------------------
Trial 966
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='sqrt',
n_estimators=114, random_state=100,
subsample=0.8))])
cv score: [0.505 0.56333333 0.6275 0.61333333 0.75158228]
----------------------------------------
Trial 967
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=78, random_state=100,
subsample=0.75))])
cv score: [0.43666667 0.67083333 0.57666667 0.48666667 0.68670886]
----------------------------------------
Trial 968
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=188, random_state=100,
subsample=0.6))])
cv score: [0.68166667 0.6075 0.71583333 0.79833333 0.78797468]
----------------------------------------
Trial 969
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=172,
random_state=100))])
cv score: [0.65333333 0.67083333 0.69916667 0.8275 0.83939873]
----------------------------------------
Trial 970
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=13, random_state=100,
subsample=0.75))])
cv score: [0.71666667 0.5875 0.70666667 0.8425 0.6835443 ]
----------------------------------------
Trial 971
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=153,
random_state=100))])
cv score: [0.695 0.6025 0.67916667 0.80916667 0.84018987]
----------------------------------------
Trial 972
----------------------------------------
Parameters {'xgb__n_estimators': 95, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=95,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.74875 0.77208333 0.85333333 0.8528481 ]
----------------------------------------
Trial 973
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6925 0.66583333 0.79166667 0.82083333 0.89952532]
----------------------------------------
Trial 974
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=161, random_state=100,
subsample=0.65))])
cv score: [0.67083333 0.64916667 0.72916667 0.79833333 0.82832278]
----------------------------------------
Trial 975
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=184, random_state=100,
subsample=0.95))])
cv score: [0.43166667 0.57166667 0.62666667 0.6625 0.72310127]
----------------------------------------
Trial 976
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=67, random_state=100,
subsample=0.6))])
cv score: [0.71833333 0.65083333 0.69916667 0.81166667 0.81882911]
----------------------------------------
Trial 977
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
n_estimators=13, random_state=100,
subsample=0.8))])
cv score: [0.60333333 0.66916667 0.80375 0.86166667 0.76107595]
----------------------------------------
Trial 978
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6975 0.73916667 0.76541667 0.85 0.86471519]
----------------------------------------
Trial 979
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=195,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.69583333 0.79583333 0.805 0.875 ]
----------------------------------------
Trial 980
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=96,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68583333 0.6575 0.725 0.75166667 0.82515823]
----------------------------------------
Trial 981
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=27,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.80583333 0.71416667 0.785 0.83833333 0.91218354]
----------------------------------------
Trial 982
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7475 0.72666667 0.7725 0.86583333 0.86629747]
----------------------------------------
Trial 983
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=106,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7225 0.72875 0.75666667 0.85583333 0.90427215]
----------------------------------------
Trial 984
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=117,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7 0.7225 0.77291667 0.8575 0.87025316]
----------------------------------------
Trial 985
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66416667 0.63833333 0.80166667 0.78083333 0.82911392]
----------------------------------------
Trial 986
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61666667 0.70625 0.69041667 0.82208333 0.76463608]
----------------------------------------
Trial 987
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.6))])
cv score: [0.69333333 0.695 0.65083333 0.795 0.82990506]
----------------------------------------
Trial 988
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=71, random_state=100,
subsample=0.85))])
cv score: [0.66833333 0.5525 0.64 0.77916667 0.76898734]
----------------------------------------
Trial 989
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=141,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77916667 0.755 0.79666667 0.85083333 0.85522152]
----------------------------------------
Trial 990
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=15, random_state=100))])
cv score: [0.70708333 0.64791667 0.77208333 0.85666667 0.8215981 ]
----------------------------------------
Trial 991
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=70,
random_state=100))])
cv score: [0.64625 0.59208333 0.71791667 0.77916667 0.77531646]
----------------------------------------
Trial 992
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=193,
random_state=100))])
cv score: [0.69416667 0.51791667 0.7975 0.67041667 0.62816456]
----------------------------------------
Trial 993
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=89,
random_state=100))])
cv score: [0.67666667 0.60083333 0.715 0.8075 0.82041139]
----------------------------------------
Trial 994
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=117,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62916667 0.6325 0.70916667 0.71416667 0.74841772]
----------------------------------------
Trial 995
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=42,
random_state=100))])
cv score: [0.68541667 0.49375 0.80791667 0.64625 0.75237342]
----------------------------------------
Trial 996
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=16, random_state=100,
subsample=0.6))])
cv score: [0.74583333 0.69333333 0.81916667 0.80666667 0.82911392]
----------------------------------------
Trial 997
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.69791667 0.75833333 0.83916667 0.88053797]
----------------------------------------
Trial 998
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='log2',
n_estimators=99,
random_state=100))])
cv score: [0.67416667 0.67416667 0.7275 0.80666667 0.84493671]
----------------------------------------
Trial 999
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=131,
random_state=100, subsample=0.8))])
cv score: [0.72 0.6925 0.73916667 0.82333333 0.83860759]
----------------------------------------
Trial 1000
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='sqrt',
n_estimators=187, random_state=100,
subsample=0.95))])
cv score: [0.59833333 0.51333333 0.69333333 0.74 0.73496835]
----------------------------------------
Trial 1001
----------------------------------------
Parameters {'xgb__n_estimators': 171, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=171,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76916667 0.7525 0.77875 0.83916667 0.87025316]
----------------------------------------
Trial 1002
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=16,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.68125 0.74458333 0.785 0.85996835]
----------------------------------------
Trial 1003
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60666667 0.62833333 0.73833333 0.76666667 0.80537975]
----------------------------------------
Trial 1004
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=17,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68833333 0.71333333 0.77166667 0.78666667 0.8164557 ]
----------------------------------------
Trial 1005
----------------------------------------
Parameters {'gb__n_estimators': 88, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=88, random_state=100,
subsample=0.95))])
cv score: [0.64 0.5875 0.71666667 0.765 0.75 ]
----------------------------------------
Trial 1006
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=173, random_state=100))])
cv score: [0.72583333 0.6775 0.77875 0.83916667 0.8346519 ]
----------------------------------------
Trial 1007
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.7225 0.77208333 0.85666667 0.85443038]
----------------------------------------
Trial 1008
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76166667 0.74083333 0.79916667 0.82416667 0.85126582]
----------------------------------------
Trial 1009
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=192,
random_state=100))])
cv score: [0.68583333 0.63583333 0.6975 0.79666667 0.82199367]
----------------------------------------
Trial 1010
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=193, random_state=100,
subsample=0.9))])
cv score: [0.62833333 0.64583333 0.71083333 0.80666667 0.74920886]
----------------------------------------
Trial 1011
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.55416667 0.6225 0.72083333 0.72166667 0.77848101]
----------------------------------------
Trial 1012
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=70, random_state=100,
subsample=0.65))])
cv score: [0.61666667 0.56583333 0.7275 0.71416667 0.58306962]
----------------------------------------
Trial 1013
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=107,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75333333 0.72 0.79 0.84 0.89161392]
----------------------------------------
Trial 1014
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=131,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76583333 0.75833333 0.78333333 0.86416667 0.86155063]
----------------------------------------
Trial 1015
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=175,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78333333 0.73875 0.78333333 0.84 0.8710443 ]
----------------------------------------
Trial 1016
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=65, random_state=100))])
cv score: [0.63166667 0.66833333 0.7425 0.77833333 0.82911392]
----------------------------------------
Trial 1017
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=172, random_state=100,
subsample=0.8))])
cv score: [0.64333333 0.61583333 0.7675 0.72166667 0.63924051]
----------------------------------------
Trial 1018
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='sqrt',
n_estimators=118, random_state=100,
subsample=0.95))])
cv score: [0.5625 0.55916667 0.66166667 0.69416667 0.67800633]
----------------------------------------
Trial 1019
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57333333 0.62 0.7575 0.71166667 0.81487342]
----------------------------------------
Trial 1020
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=57,
random_state=100))])
cv score: [0.69416667 0.57166667 0.71416667 0.7675 0.69857595]
----------------------------------------
Trial 1021
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=140,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6425 0.67833333 0.765 0.715 0.78955696]
----------------------------------------
Trial 1022
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=111,
random_state=100))])
cv score: [0.53583333 0.57666667 0.70833333 0.69 0.73259494]
----------------------------------------
Trial 1023
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=180,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.66916667 0.7675 0.795 0.88607595]
----------------------------------------
Trial 1024
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73625 0.71625 0.73625 0.85916667 0.83148734]
----------------------------------------
Trial 1025
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=181, random_state=100,
subsample=0.65))])
cv score: [0.67333333 0.66666667 0.71833333 0.80166667 0.83860759]
----------------------------------------
Trial 1026
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.755 0.725 0.77 0.86166667 0.86946203]
----------------------------------------
Trial 1027
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=35,
random_state=100))])
cv score: [0.69083333 0.48916667 0.78083333 0.67083333 0.63607595]
----------------------------------------
Trial 1028
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=115,
random_state=100))])
cv score: [0.58416667 0.6625 0.705 0.8325 0.83306962]
----------------------------------------
Trial 1029
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=53, random_state=100,
subsample=0.7))])
cv score: [0.72666667 0.65875 0.80583333 0.82833333 0.81803797]
----------------------------------------
Trial 1030
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75916667 0.71166667 0.805 0.83083333 0.88212025]
----------------------------------------
Trial 1031
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=59, random_state=100))])
cv score: [0.74291667 0.67958333 0.775 0.82958333 0.85126582]
----------------------------------------
Trial 1032
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
n_estimators=24, random_state=100,
subsample=0.95))])
cv score: [0.60666667 0.56625 0.61 0.73125 0.72705696]
----------------------------------------
Trial 1033
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=102,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.6875 0.775 0.81916667 0.91139241]
----------------------------------------
Trial 1034
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=193, random_state=100))])
cv score: [0.68666667 0.63416667 0.72083333 0.79666667 0.83860759]
----------------------------------------
Trial 1035
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='sqrt',
n_estimators=33, random_state=100,
subsample=0.8))])
cv score: [0.66833333 0.645 0.70916667 0.83083333 0.79667722]
----------------------------------------
Trial 1036
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=60,
random_state=100))])
cv score: [0.62416667 0.56083333 0.705 0.69916667 0.77927215]
----------------------------------------
Trial 1037
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=54,
random_state=100, subsample=0.7))])
cv score: [0.68416667 0.6375 0.77583333 0.83416667 0.79984177]
----------------------------------------
Trial 1038
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76208333 0.73 0.78583333 0.82416667 0.8397943 ]
----------------------------------------
Trial 1039
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=134,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6425 0.625 0.745 0.71583333 0.79272152]
----------------------------------------
Trial 1040
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=50, random_state=100))])
cv score: [0.655 0.68083333 0.72 0.78583333 0.83148734]
----------------------------------------
Trial 1041
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.565 0.62458333 0.65125 0.80416667 0.73378165]
----------------------------------------
Trial 1042
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=76, random_state=100))])
cv score: [0.6975 0.66916667 0.725 0.79416667 0.86471519]
----------------------------------------
Trial 1043
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.50833333 0.68291667 0.66041667 0.7725 0.65387658]
----------------------------------------
Trial 1044
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.70083333 0.80083333 0.8175 0.8528481 ]
----------------------------------------
Trial 1045
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=164,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.70583333 0.79916667 0.83083333 0.89873418]
----------------------------------------
Trial 1046
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=162,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.6225 0.725 0.76083333 0.81408228]
----------------------------------------
Trial 1047
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=36, random_state=100,
subsample=0.7))])
cv score: [0.7 0.62 0.66083333 0.81333333 0.79509494]
----------------------------------------
Trial 1048
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=159, random_state=100,
subsample=0.75))])
cv score: [0.71916667 0.67833333 0.78083333 0.82083333 0.83386076]
----------------------------------------
Trial 1049
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=101,
random_state=100, subsample=0.9))])
cv score: [0.685 0.62333333 0.71916667 0.75416667 0.65981013]
----------------------------------------
Trial 1050
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=152, random_state=100,
subsample=0.75))])
cv score: [0.68916667 0.59416667 0.74 0.81583333 0.78797468]
----------------------------------------
Trial 1051
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=121,
random_state=100))])
cv score: [0.68666667 0.49375 0.81 0.64708333 0.75316456]
----------------------------------------
Trial 1052
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='sqrt',
n_estimators=82, random_state=100,
subsample=0.8))])
cv score: [0.66666667 0.71541667 0.69 0.83666667 0.8306962 ]
----------------------------------------
Trial 1053
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=135,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.6625 0.78876582]
----------------------------------------
Trial 1054
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63666667 0.595 0.755 0.66 0.82357595]
----------------------------------------
Trial 1055
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=131,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.73875 0.77125 0.8625 0.86075949]
----------------------------------------
Trial 1056
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=57,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.7475 0.77708333 0.845 0.88053797]
----------------------------------------
Trial 1057
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76166667 0.71583333 0.79083333 0.85416667 0.88449367]
----------------------------------------
Trial 1058
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71416667 0.71791667 0.765 0.85833333 0.88212025]
----------------------------------------
Trial 1059
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=126, random_state=100,
subsample=0.95))])
cv score: [0.695 0.515 0.77916667 0.775 0.76582278]
----------------------------------------
Trial 1060
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='log2',
n_estimators=59, random_state=100,
subsample=0.9))])
cv score: [0.66166667 0.585 0.67583333 0.79583333 0.81329114]
----------------------------------------
Trial 1061
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=160, random_state=100))])
cv score: [0.78333333 0.68083333 0.76666667 0.85083333 0.87420886]
----------------------------------------
Trial 1062
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=108,
random_state=100))])
cv score: [0.56041667 0.66125 0.69 0.83041667 0.79469937]
----------------------------------------
Trial 1063
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=122, random_state=100))])
cv score: [0.72166667 0.67958333 0.78458333 0.83625 0.8306962 ]
----------------------------------------
Trial 1064
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59166667 0.66916667 0.75333333 0.73666667 0.7943038 ]
----------------------------------------
Trial 1065
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72333333 0.685 0.76833333 0.81083333 0.88765823]
----------------------------------------
Trial 1066
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=165,
random_state=100))])
cv score: [0.64416667 0.58083333 0.73291667 0.7825 0.79351266]
----------------------------------------
Trial 1067
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=59, random_state=100,
subsample=0.6))])
cv score: [0.6775 0.65333333 0.68 0.7825 0.78876582]
----------------------------------------
Trial 1068
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=41,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6925 0.66916667 0.71416667 0.74083333 0.82911392]
----------------------------------------
Trial 1069
----------------------------------------
Parameters {'gb__n_estimators': 88, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=88, random_state=100,
subsample=0.95))])
cv score: [0.75083333 0.59708333 0.80791667 0.825 0.82080696]
----------------------------------------
Trial 1070
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=25,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68416667 0.72 0.7575 0.72333333 0.7539557 ]
----------------------------------------
Trial 1071
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=16,
random_state=100, subsample=0.6))])
cv score: [0.7725 0.70666667 0.7925 0.765 0.79509494]
----------------------------------------
Trial 1072
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='sqrt',
n_estimators=14, random_state=100,
subsample=0.65))])
cv score: [0.68 0.64083333 0.70083333 0.76 0.86392405]
----------------------------------------
Trial 1073
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=156,
random_state=100))])
cv score: [0.65333333 0.67166667 0.69583333 0.81916667 0.84493671]
----------------------------------------
Trial 1074
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=150, random_state=100))])
cv score: [0.68666667 0.65333333 0.71916667 0.8075 0.83860759]
----------------------------------------
Trial 1075
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6025 0.66 0.77166667 0.76083333 0.80221519]
----------------------------------------
Trial 1076
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=138, random_state=100,
subsample=0.65))])
cv score: [0.7375 0.6625 0.77333333 0.8325 0.82911392]
----------------------------------------
Trial 1077
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='sqrt',
n_estimators=73, random_state=100,
subsample=0.65))])
cv score: [0.69333333 0.60666667 0.715 0.78166667 0.89003165]
----------------------------------------
Trial 1078
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=92, random_state=100,
subsample=0.75))])
cv score: [0.64666667 0.66833333 0.7075 0.84333333 0.84414557]
----------------------------------------
Trial 1079
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=112,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75375 0.74291667 0.76583333 0.84916667 0.87262658]
----------------------------------------
Trial 1080
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=135,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.6625 0.78876582]
----------------------------------------
Trial 1081
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=80, random_state=100,
subsample=0.8))])
cv score: [0.6825 0.61 0.7275 0.80833333 0.59810127]
----------------------------------------
Trial 1082
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
n_estimators=138, random_state=100,
subsample=0.9))])
cv score: [0.755 0.6975 0.74041667 0.84708333 0.89398734]
----------------------------------------
Trial 1083
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62583333 0.65 0.71833333 0.725 0.83386076]
----------------------------------------
Trial 1084
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=178, random_state=100,
subsample=0.65))])
cv score: [0.72083333 0.67916667 0.7675 0.82666667 0.81091772]
----------------------------------------
Trial 1085
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=115, random_state=100,
subsample=0.6))])
cv score: [0.66833333 0.65666667 0.73916667 0.8125 0.81962025]
----------------------------------------
Trial 1086
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=70,
random_state=100))])
cv score: [0.55791667 0.67791667 0.71041667 0.82625 0.80023734]
----------------------------------------
Trial 1087
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=41, random_state=100))])
cv score: [0.64208333 0.66166667 0.71166667 0.81291667 0.82238924]
----------------------------------------
Trial 1088
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='sqrt',
n_estimators=80, random_state=100,
subsample=0.7))])
cv score: [0.64166667 0.62 0.69416667 0.71583333 0.77610759]
----------------------------------------
Trial 1089
----------------------------------------
Parameters {'xgb__n_estimators': 95, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=95,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61666667 0.6575 0.675 0.77 0.79193038]
----------------------------------------
Trial 1090
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=22, random_state=100,
subsample=0.8))])
cv score: [0.665 0.66333333 0.80416667 0.825 0.75158228]
----------------------------------------
Trial 1091
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=36,
random_state=100))])
cv score: [0.6425 0.6225 0.695 0.66916667 0.71360759]
----------------------------------------
Trial 1092
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=21,
random_state=100))])
cv score: [0.75166667 0.64166667 0.685 0.7675 0.79193038]
----------------------------------------
Trial 1093
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78666667 0.73333333 0.78541667 0.8475 0.86234177]
----------------------------------------
Trial 1094
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=157,
random_state=100))])
cv score: [0.62333333 0.625 0.68583333 0.72583333 0.68591772]
----------------------------------------
Trial 1095
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=174, random_state=100))])
cv score: [0.70875 0.61625 0.7375 0.82666667 0.79905063]
----------------------------------------
Trial 1096
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
n_estimators=65, random_state=100,
subsample=0.65))])
cv score: [0.72083333 0.68416667 0.7925 0.83083333 0.83544304]
----------------------------------------
Trial 1097
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=19, random_state=100))])
cv score: [0.595 0.67083333 0.665 0.78 0.82674051]
----------------------------------------
Trial 1098
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=181, random_state=100))])
cv score: [0.67333333 0.65833333 0.71666667 0.81 0.85443038]
----------------------------------------
Trial 1099
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=163, random_state=100))])
cv score: [0.65666667 0.655 0.70416667 0.82 0.84968354]
----------------------------------------
Trial 1100
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
n_estimators=152, random_state=100,
subsample=0.95))])
cv score: [0.77 0.71125 0.74541667 0.83666667 0.89161392]
----------------------------------------
Trial 1101
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=195, random_state=100))])
cv score: [0.67833333 0.65916667 0.71833333 0.81083333 0.85601266]
----------------------------------------
Trial 1102
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=57,
random_state=100))])
cv score: [0.77666667 0.67833333 0.74666667 0.845 0.81170886]
----------------------------------------
Trial 1103
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=135, random_state=100))])
cv score: [0.745 0.68458333 0.735 0.85916667 0.88132911]
----------------------------------------
Trial 1104
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64416667 0.65416667 0.70916667 0.745 0.74446203]
----------------------------------------
Trial 1105
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71416667 0.68 0.79083333 0.81 0.87737342]
----------------------------------------
Trial 1106
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=151,
random_state=100))])
cv score: [0.63666667 0.6325 0.7275 0.76833333 0.77294304]
----------------------------------------
Trial 1107
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7125 0.7525 0.80458333 0.84791667 0.86985759]
----------------------------------------
Trial 1108
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=10, random_state=100))])
cv score: [0.66916667 0.6375 0.72208333 0.81708333 0.83386076]
----------------------------------------
Trial 1109
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=183, random_state=100))])
cv score: [0.665 0.66583333 0.7025 0.81833333 0.85601266]
----------------------------------------
Trial 1110
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=79, random_state=100))])
cv score: [0.65166667 0.67083333 0.7025 0.8225 0.85126582]
----------------------------------------
Trial 1111
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='log2',
n_estimators=111, random_state=100,
subsample=0.6))])
cv score: [0.69083333 0.58583333 0.67333333 0.82583333 0.74367089]
----------------------------------------
Trial 1112
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='log2',
n_estimators=173, random_state=100,
subsample=0.6))])
cv score: [0.66333333 0.62833333 0.73 0.79166667 0.82436709]
----------------------------------------
Trial 1113
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=104,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63916667 0.685 0.78333333 0.775 0.79667722]
----------------------------------------
Trial 1114
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
n_estimators=80, random_state=100,
subsample=0.65))])
cv score: [0.6525 0.6475 0.69833333 0.73 0.75791139]
----------------------------------------
Trial 1115
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=156, random_state=100))])
cv score: [0.7025 0.62041667 0.7475 0.80958333 0.79549051]
----------------------------------------
Trial 1116
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=12,
random_state=100))])
cv score: [0.7425 0.5575 0.67 0.77083333 0.80696203]
----------------------------------------
Trial 1117
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=185, random_state=100))])
cv score: [0.6775 0.62333333 0.72916667 0.8 0.84256329]
----------------------------------------
Trial 1118
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.68583333 0.77083333 0.7575 0.80617089]
----------------------------------------
Trial 1119
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.74416667 0.7875 0.83 0.87381329]
----------------------------------------
Trial 1120
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=103, random_state=100))])
cv score: [0.695 0.62916667 0.72166667 0.815 0.82911392]
----------------------------------------
Trial 1121
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71166667 0.73 0.77041667 0.86916667 0.85917722]
----------------------------------------
Trial 1122
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=71,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65583333 0.70666667 0.79083333 0.80916667 0.84335443]
----------------------------------------
Trial 1123
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=169,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 1124
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=81,
random_state=100))])
cv score: [0.63166667 0.59416667 0.71958333 0.76958333 0.77333861]
----------------------------------------
Trial 1125
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='sqrt',
n_estimators=19, random_state=100,
subsample=0.95))])
cv score: [0.585 0.61958333 0.75083333 0.795 0.81803797]
----------------------------------------
Trial 1126
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=48, random_state=100,
subsample=0.7))])
cv score: [0.68083333 0.73166667 0.65583333 0.835 0.84731013]
----------------------------------------
Trial 1127
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=33, random_state=100))])
cv score: [0.68833333 0.65916667 0.69416667 0.80083333 0.87420886]
----------------------------------------
Trial 1128
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=113,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.685 0.68833333 0.78083333 0.8075 0.84731013]
----------------------------------------
Trial 1129
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66708333 0.73708333 0.76375 0.85791667 0.87579114]
----------------------------------------
Trial 1130
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=142, random_state=100,
subsample=0.7))])
cv score: [0.65416667 0.58583333 0.65666667 0.74666667 0.73892405]
----------------------------------------
Trial 1131
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=117, random_state=100))])
cv score: [0.72125 0.67625 0.78333333 0.83375 0.83188291]
----------------------------------------
Trial 1132
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=25,
random_state=100,
subsample=0.95))])
cv score: [0.74083333 0.645 0.80333333 0.82916667 0.75237342]
----------------------------------------
Trial 1133
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=161, random_state=100))])
cv score: [0.6975 0.6175 0.74583333 0.81875 0.79786392]
----------------------------------------
Trial 1134
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=178,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65666667 0.6375 0.77083333 0.74 0.78085443]
----------------------------------------
Trial 1135
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=154, random_state=100))])
cv score: [0.62166667 0.69166667 0.70166667 0.8 0.83544304]
----------------------------------------
Trial 1136
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.7275 0.79166667 0.85416667 0.88212025]
----------------------------------------
Trial 1137
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=129,
random_state=100))])
cv score: [0.6825 0.58333333 0.69583333 0.79083333 0.79588608]
----------------------------------------
Trial 1138
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.695 0.67 0.775 0.77166667 0.87420886]
----------------------------------------
Trial 1139
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=65, random_state=100,
subsample=0.8))])
cv score: [0.55916667 0.5775 0.72833333 0.655 0.67800633]
----------------------------------------
Trial 1140
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=125, random_state=100,
subsample=0.95))])
cv score: [0.62 0.54333333 0.66083333 0.7725 0.73101266]
----------------------------------------
Trial 1141
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=161,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.595 0.60583333 0.74625 0.72833333 0.75791139]
----------------------------------------
Trial 1142
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=18,
random_state=100))])
cv score: [0.52791667 0.67458333 0.69583333 0.82541667 0.80933544]
----------------------------------------
Trial 1143
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='log2',
n_estimators=179, random_state=100,
subsample=0.7))])
cv score: [0.63166667 0.60666667 0.67166667 0.77 0.78243671]
----------------------------------------
Trial 1144
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=176, random_state=100,
subsample=0.75))])
cv score: [0.695 0.595 0.6775 0.725 0.72151899]
----------------------------------------
Trial 1145
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=19,
random_state=100))])
cv score: [0.62583333 0.57833333 0.67791667 0.77333333 0.83821203]
----------------------------------------
Trial 1146
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=137, random_state=100))])
cv score: [0.68916667 0.65666667 0.72583333 0.81333333 0.85917722]
----------------------------------------
Trial 1147
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=48,
random_state=100))])
cv score: [0.69 0.50125 0.72541667 0.65083333 0.63330696]
----------------------------------------
Trial 1148
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='log2',
n_estimators=161, random_state=100,
subsample=0.6))])
cv score: [0.58166667 0.65916667 0.62 0.69916667 0.74367089]
----------------------------------------
Trial 1149
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=197, random_state=100,
subsample=0.6))])
cv score: [0.5675 0.55 0.57333333 0.62333333 0.68512658]
----------------------------------------
Trial 1150
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=143, random_state=100,
subsample=0.9))])
cv score: [0.54166667 0.55333333 0.66916667 0.64833333 0.74129747]
----------------------------------------
Trial 1151
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=156,
random_state=100))])
cv score: [0.61916667 0.67166667 0.69833333 0.81916667 0.82911392]
----------------------------------------
Trial 1152
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=68,
random_state=100))])
cv score: [0.66541667 0.50541667 0.79666667 0.66333333 0.78876582]
----------------------------------------
Trial 1153
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=146,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69666667 0.6725 0.77666667 0.78583333 0.84889241]
----------------------------------------
Trial 1154
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=183,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70375 0.73 0.76125 0.87333333 0.86313291]
----------------------------------------
Trial 1155
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=112, random_state=100,
subsample=0.75))])
cv score: [0.60833333 0.57583333 0.70083333 0.68666667 0.65348101]
----------------------------------------
Trial 1156
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=62, random_state=100,
subsample=0.8))])
cv score: [0.5775 0.59916667 0.71666667 0.675 0.79746835]
----------------------------------------
Trial 1157
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=22,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79916667 0.68333333 0.7675 0.80333333 0.83860759]
----------------------------------------
Trial 1158
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=198,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6625 0.65 0.78083333 0.73916667 0.86708861]
----------------------------------------
Trial 1159
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64416667 0.59083333 0.7525 0.76333333 0.80458861]
----------------------------------------
Trial 1160
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.70833333 0.77166667 0.8075 0.84651899]
----------------------------------------
Trial 1161
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='log2',
n_estimators=76, random_state=100,
subsample=0.8))])
cv score: [0.59166667 0.66041667 0.68875 0.78208333 0.77333861]
----------------------------------------
Trial 1162
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69125 0.73625 0.77291667 0.865 0.85205696]
----------------------------------------
Trial 1163
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=108,
random_state=100))])
cv score: [0.51458333 0.66708333 0.71458333 0.82166667 0.75870253]
----------------------------------------
Trial 1164
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=153,
random_state=100))])
cv score: [0.58583333 0.6625 0.70833333 0.8325 0.82753165]
----------------------------------------
Trial 1165
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.9))])
cv score: [0.69416667 0.58916667 0.67416667 0.81666667 0.75553797]
----------------------------------------
Trial 1166
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=117,
random_state=100))])
cv score: [0.56708333 0.65875 0.69083333 0.83041667 0.8034019 ]
----------------------------------------
Trial 1167
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=87, random_state=100))])
cv score: [0.73333333 0.69541667 0.72583333 0.85166667 0.87816456]
----------------------------------------
Trial 1168
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.68833333 0.70583333 0.76083333 0.78955696]
----------------------------------------
Trial 1169
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=49,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64666667 0.615 0.7375 0.705 0.80063291]
----------------------------------------
Trial 1170
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=110,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66583333 0.695 0.71083333 0.77666667 0.84968354]
----------------------------------------
Trial 1171
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.83875 0.75458333 0.77166667 0.83916667 0.87618671]
----------------------------------------
Trial 1172
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=162,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71083333 0.68833333 0.79916667 0.7775 0.86867089]
----------------------------------------
Trial 1173
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=103, random_state=100,
subsample=0.9))])
cv score: [0.61083333 0.59916667 0.705 0.75333333 0.76424051]
----------------------------------------
Trial 1174
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=65, random_state=100,
subsample=0.75))])
cv score: [0.64 0.70916667 0.6975 0.66333333 0.71914557]
----------------------------------------
Trial 1175
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=146, random_state=100,
subsample=0.65))])
cv score: [0.59833333 0.59666667 0.705 0.79083333 0.81487342]
----------------------------------------
Trial 1176
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=110,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62916667 0.60583333 0.77833333 0.75416667 0.80933544]
----------------------------------------
Trial 1177
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=35,
random_state=100))])
cv score: [0.69083333 0.50125 0.72791667 0.64833333 0.63212025]
----------------------------------------
Trial 1178
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=154,
random_state=100,
subsample=0.75))])
cv score: [0.6475 0.67416667 0.725 0.7725 0.68591772]
----------------------------------------
Trial 1179
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
n_estimators=179, random_state=100,
subsample=0.6))])
cv score: [0.74083333 0.67916667 0.78333333 0.8425 0.81803797]
----------------------------------------
Trial 1180
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 1181
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='log2',
n_estimators=119, random_state=100,
subsample=0.8))])
cv score: [0.65666667 0.65416667 0.66583333 0.83333333 0.84731013]
----------------------------------------
Trial 1182
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=166,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.6575 0.80083333 0.77916667 0.85363924]
----------------------------------------
Trial 1183
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.73666667 0.7875 0.84916667 0.8931962 ]
----------------------------------------
Trial 1184
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.75))])
cv score: [0.70416667 0.6375 0.71333333 0.795 0.80537975]
----------------------------------------
Trial 1185
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=84,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68166667 0.71916667 0.78 0.84666667 0.88291139]
----------------------------------------
Trial 1186
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=138,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59458333 0.71083333 0.64583333 0.83416667 0.78599684]
----------------------------------------
Trial 1187
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='sqrt',
n_estimators=159, random_state=100,
subsample=0.7))])
cv score: [0.66083333 0.5875 0.60333333 0.72166667 0.5443038 ]
----------------------------------------
Trial 1188
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=55,
random_state=100))])
cv score: [0.64875 0.59416667 0.71625 0.77291667 0.78164557]
----------------------------------------
Trial 1189
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76166667 0.7175 0.80666667 0.8375 0.89240506]
----------------------------------------
Trial 1190
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=198,
random_state=100))])
cv score: [0.6925 0.58916667 0.72 0.78916667 0.80300633]
----------------------------------------
Trial 1191
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=38, random_state=100))])
cv score: [0.6225 0.68833333 0.72875 0.81416667 0.84810127]
----------------------------------------
Trial 1192
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=179,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74083333 0.6975 0.7875 0.82 0.86550633]
----------------------------------------
Trial 1193
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=163, random_state=100,
subsample=0.7))])
cv score: [0.63416667 0.58833333 0.66333333 0.74416667 0.73101266]
----------------------------------------
Trial 1194
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=36, random_state=100))])
cv score: [0.62416667 0.67666667 0.72375 0.81583333 0.85047468]
----------------------------------------
Trial 1195
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
n_estimators=170,
random_state=100))])
cv score: [0.72166667 0.64083333 0.76333333 0.80208333 0.77452532]
----------------------------------------
Trial 1196
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='log2',
n_estimators=144, random_state=100,
subsample=0.6))])
cv score: [0.59583333 0.69333333 0.68166667 0.78083333 0.81962025]
----------------------------------------
Trial 1197
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, n_estimators=174,
random_state=100,
subsample=0.65))])
cv score: [0.70083333 0.68166667 0.72583333 0.76833333 0.67879747]
----------------------------------------
Trial 1198
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=161,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76083333 0.71416667 0.78166667 0.84416667 0.87420886]
----------------------------------------
Trial 1199
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=118,
random_state=100))])
cv score: [0.51208333 0.66375 0.71208333 0.81416667 0.75949367]
----------------------------------------
Trial 1200
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='log2',
n_estimators=59, random_state=100,
subsample=0.65))])
cv score: [0.63083333 0.70666667 0.65916667 0.80333333 0.78797468]
----------------------------------------
Trial 1201
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67666667 0.66166667 0.74833333 0.7525 0.80458861]
----------------------------------------
Trial 1202
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63166667 0.66333333 0.7325 0.73333333 0.78639241]
----------------------------------------
Trial 1203
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77208333 0.71125 0.77541667 0.84083333 0.85205696]
----------------------------------------
Trial 1204
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=37, random_state=100,
subsample=0.8))])
cv score: [0.72833333 0.6725 0.75833333 0.81416667 0.83227848]
----------------------------------------
Trial 1205
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=134,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73333333 0.71666667 0.77916667 0.83666667 0.89556962]
----------------------------------------
Trial 1206
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='log2',
n_estimators=189, random_state=100,
subsample=0.75))])
cv score: [0.63 0.67833333 0.6975 0.8225 0.8125 ]
----------------------------------------
Trial 1207
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.72083333 0.76958333 0.86 0.8528481 ]
----------------------------------------
Trial 1208
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=105, random_state=100))])
cv score: [0.6575 0.68083333 0.74083333 0.7825 0.83860759]
----------------------------------------
Trial 1209
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=36, random_state=100))])
cv score: [0.63416667 0.64125 0.7575 0.83666667 0.79746835]
----------------------------------------
Trial 1210
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.55125 0.72083333 0.66666667 0.82083333 0.77333861]
----------------------------------------
Trial 1211
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76666667 0.75 0.79041667 0.83666667 0.86787975]
----------------------------------------
Trial 1212
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=32,
random_state=100))])
cv score: [0.69208333 0.51833333 0.79916667 0.6675 0.63212025]
----------------------------------------
Trial 1213
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=152,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69958333 0.70583333 0.74958333 0.85083333 0.86787975]
----------------------------------------
Trial 1214
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72666667 0.735 0.79 0.85166667 0.90901899]
----------------------------------------
Trial 1215
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=105,
random_state=100))])
cv score: [0.69291667 0.50125 0.72541667 0.64875 0.63370253]
----------------------------------------
Trial 1216
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72833333 0.68083333 0.79416667 0.815 0.88607595]
----------------------------------------
Trial 1217
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=165, random_state=100,
subsample=0.8))])
cv score: [0.60583333 0.58583333 0.7225 0.78166667 0.82990506]
----------------------------------------
Trial 1218
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67083333 0.72125 0.74041667 0.85333333 0.84454114]
----------------------------------------
Trial 1219
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=58,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70666667 0.6725 0.74083333 0.81083333 0.87183544]
----------------------------------------
Trial 1220
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=92,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.67666667 0.78 0.83916667 0.93037975]
----------------------------------------
Trial 1221
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=106,
random_state=100))])
cv score: [0.61416667 0.67708333 0.69416667 0.81 0.82990506]
----------------------------------------
Trial 1222
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=70, random_state=100))])
cv score: [0.68166667 0.61583333 0.725 0.78666667 0.82436709]
----------------------------------------
Trial 1223
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=104,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 1224
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61833333 0.7025 0.735 0.74416667 0.79193038]
----------------------------------------
Trial 1225
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=107,
random_state=100))])
cv score: [0.52083333 0.55 0.6925 0.62083333 0.72310127]
----------------------------------------
Trial 1226
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=59,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75333333 0.74208333 0.7725 0.86583333 0.90308544]
----------------------------------------
Trial 1227
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
n_estimators=189,
random_state=100))])
cv score: [0.68416667 0.52333333 0.72583333 0.71208333 0.62618671]
----------------------------------------
Trial 1228
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=169, random_state=100))])
cv score: [0.70083333 0.6375 0.72583333 0.80833333 0.8306962 ]
----------------------------------------
Trial 1229
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.65583333 0.7725 0.8025 0.87262658]
----------------------------------------
Trial 1230
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.65))])
cv score: [0.66083333 0.58666667 0.71666667 0.81333333 0.83939873]
----------------------------------------
Trial 1231
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=22, random_state=100,
subsample=0.9))])
cv score: [0.65666667 0.50166667 0.65083333 0.75666667 0.76028481]
----------------------------------------
Trial 1232
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=186,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59166667 0.635 0.78 0.75333333 0.80933544]
----------------------------------------
Trial 1233
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=194, random_state=100,
subsample=0.95))])
cv score: [0.67583333 0.58166667 0.74083333 0.7825 0.78560127]
----------------------------------------
Trial 1234
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=65, random_state=100))])
cv score: [0.66583333 0.6 0.72583333 0.78833333 0.83781646]
----------------------------------------
Trial 1235
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=136,
random_state=100))])
cv score: [0.62916667 0.67666667 0.70333333 0.81583333 0.82674051]
----------------------------------------
Trial 1236
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=56, random_state=100))])
cv score: [0.72958333 0.68041667 0.77333333 0.84041667 0.83860759]
----------------------------------------
Trial 1237
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=76,
random_state=100))])
cv score: [0.55708333 0.67875 0.70125 0.82041667 0.7994462 ]
----------------------------------------
Trial 1238
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=54,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.75166667 0.79833333 0.86416667 0.86234177]
----------------------------------------
Trial 1239
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='log2',
n_estimators=170, random_state=100,
subsample=0.9))])
cv score: [0.63166667 0.58166667 0.68 0.74416667 0.77452532]
----------------------------------------
Trial 1240
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=70,
random_state=100))])
cv score: [0.68458333 0.51375 0.82291667 0.66791667 0.66574367]
----------------------------------------
Trial 1241
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=154, random_state=100,
subsample=0.8))])
cv score: [0.56083333 0.575 0.67666667 0.67833333 0.7318038 ]
----------------------------------------
Trial 1242
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
n_estimators=151, random_state=100,
subsample=0.8))])
cv score: [0.71666667 0.64833333 0.81083333 0.8425 0.80379747]
----------------------------------------
Trial 1243
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=41, random_state=100))])
cv score: [0.75875 0.69125 0.73083333 0.86166667 0.87658228]
----------------------------------------
Trial 1244
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63916667 0.55666667 0.73166667 0.74833333 0.73338608]
----------------------------------------
Trial 1245
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=104,
random_state=100))])
cv score: [0.5775 0.66416667 0.69416667 0.83083333 0.83702532]
----------------------------------------
Trial 1246
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75291667 0.755 0.79541667 0.85208333 0.85443038]
----------------------------------------
Trial 1247
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=163,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.685 0.66333333 0.76666667 0.81 0.85601266]
----------------------------------------
Trial 1248
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=29, random_state=100))])
cv score: [0.63375 0.62791667 0.71708333 0.77166667 0.83939873]
----------------------------------------
Trial 1249
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
n_estimators=42, random_state=100,
subsample=0.95))])
cv score: [0.69333333 0.6625 0.7225 0.76916667 0.80221519]
----------------------------------------
Trial 1250
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
n_estimators=142, random_state=100,
subsample=0.6))])
cv score: [0.605 0.7075 0.69416667 0.84333333 0.71598101]
----------------------------------------
Trial 1251
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=20, random_state=100))])
cv score: [0.76333333 0.68625 0.72125 0.87666667 0.87262658]
----------------------------------------
Trial 1252
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=64, random_state=100,
subsample=0.75))])
cv score: [0.64333333 0.71083333 0.69333333 0.66416667 0.71835443]
----------------------------------------
Trial 1253
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=131, random_state=100))])
cv score: [0.66166667 0.65583333 0.7075 0.82166667 0.84810127]
----------------------------------------
Trial 1254
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='sqrt',
n_estimators=116, random_state=100,
subsample=0.85))])
cv score: [0.63583333 0.59 0.65916667 0.77916667 0.73101266]
----------------------------------------
Trial 1255
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=73, random_state=100,
subsample=0.65))])
cv score: [0.64833333 0.66583333 0.70166667 0.81 0.86075949]
----------------------------------------
Trial 1256
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=94, random_state=100,
subsample=0.85))])
cv score: [0.65166667 0.64083333 0.70916667 0.78416667 0.82041139]
----------------------------------------
Trial 1257
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='log2',
n_estimators=81, random_state=100,
subsample=0.9))])
cv score: [0.59583333 0.63 0.60833333 0.71916667 0.60759494]
----------------------------------------
Trial 1258
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=96, random_state=100,
subsample=0.85))])
cv score: [0.7125 0.59916667 0.7175 0.80916667 0.84256329]
----------------------------------------
Trial 1259
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=58, random_state=100,
subsample=0.6))])
cv score: [0.7225 0.64833333 0.6825 0.725 0.77452532]
----------------------------------------
Trial 1260
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=163, random_state=100,
subsample=0.95))])
cv score: [0.61166667 0.30333333 0.68 0.72416667 0.7056962 ]
----------------------------------------
Trial 1261
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=14, random_state=100))])
cv score: [0.7375 0.64625 0.77458333 0.85291667 0.81329114]
----------------------------------------
Trial 1262
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=159, random_state=100,
subsample=0.9))])
cv score: [0.605 0.56 0.63583333 0.68666667 0.79905063]
----------------------------------------
Trial 1263
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=163,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7225 0.725 0.75041667 0.87083333 0.85047468]
----------------------------------------
Trial 1264
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=84, random_state=100))])
cv score: [0.7525 0.69833333 0.77833333 0.83583333 0.85996835]
----------------------------------------
Trial 1265
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=69,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53708333 0.605 0.64541667 0.79083333 0.73852848]
----------------------------------------
Trial 1266
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=47, random_state=100))])
cv score: [0.67833333 0.63083333 0.7475 0.81333333 0.83306962]
----------------------------------------
Trial 1267
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77666667 0.75 0.78666667 0.85083333 0.85443038]
----------------------------------------
Trial 1268
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=179,
random_state=100))])
cv score: [0.65583333 0.66833333 0.6975 0.82833333 0.84177215]
----------------------------------------
Trial 1269
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.46333333 0.69416667 0.75458333 0.82708333 0.78797468]
----------------------------------------
Trial 1270
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74833333 0.72458333 0.7725 0.83583333 0.83860759]
----------------------------------------
Trial 1271
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='log2',
n_estimators=161, random_state=100,
subsample=0.95))])
cv score: [0.66916667 0.61666667 0.685 0.80666667 0.84018987]
----------------------------------------
Trial 1272
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68791667 0.74416667 0.75791667 0.85875 0.82674051]
----------------------------------------
Trial 1273
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='sqrt',
n_estimators=175, random_state=100,
subsample=0.7))])
cv score: [0.63166667 0.67166667 0.595 0.68416667 0.68275316]
----------------------------------------
Trial 1274
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=133, random_state=100,
subsample=0.8))])
cv score: [0.65583333 0.60416667 0.70666667 0.81833333 0.83860759]
----------------------------------------
Trial 1275
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=104,
random_state=100,
subsample=0.95))])
cv score: [0.735 0.67583333 0.755 0.82666667 0.7721519 ]
----------------------------------------
Trial 1276
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=50,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77833333 0.73 0.78041667 0.85166667 0.9153481 ]
----------------------------------------
Trial 1277
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=51,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.80666667 0.7475 0.78875 0.82583333 0.92009494]
----------------------------------------
Trial 1278
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=132,
random_state=100))])
cv score: [0.51625 0.67458333 0.70208333 0.81 0.76265823]
----------------------------------------
Trial 1279
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=30, random_state=100))])
cv score: [0.76625 0.68916667 0.73791667 0.85541667 0.89398734]
----------------------------------------
Trial 1280
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
n_estimators=168, random_state=100,
subsample=0.95))])
cv score: [0.6675 0.65 0.76166667 0.7625 0.7278481 ]
----------------------------------------
Trial 1281
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64416667 0.65833333 0.75166667 0.72916667 0.78401899]
----------------------------------------
Trial 1282
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6525 0.665 0.77416667 0.79083333 0.83702532]
----------------------------------------
Trial 1283
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, n_estimators=76,
random_state=100))])
cv score: [0.73916667 0.64666667 0.765 0.78666667 0.7903481 ]
----------------------------------------
Trial 1284
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7675 0.705 0.79 0.82833333 0.89082278]
----------------------------------------
Trial 1285
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='log2',
n_estimators=12, random_state=100,
subsample=0.85))])
cv score: [0.60583333 0.59083333 0.65583333 0.77416667 0.68117089]
----------------------------------------
Trial 1286
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=181, random_state=100,
subsample=0.65))])
cv score: [0.70083333 0.65333333 0.73 0.81583333 0.83702532]
----------------------------------------
Trial 1287
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=14,
random_state=100))])
cv score: [0.68708333 0.48916667 0.77666667 0.67166667 0.63924051]
----------------------------------------
Trial 1288
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=50,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.695 0.79583333 0.8175 0.86075949]
----------------------------------------
Trial 1289
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=62,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.7525 0.78875 0.8525 0.85917722]
----------------------------------------
Trial 1290
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=143, random_state=100,
subsample=0.65))])
cv score: [0.6025 0.67916667 0.69666667 0.83416667 0.82278481]
----------------------------------------
Trial 1291
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=31,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 1292
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5575 0.64333333 0.78833333 0.73333333 0.77689873]
----------------------------------------
Trial 1293
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=167, random_state=100,
subsample=0.6))])
cv score: [0.48291667 0.585 0.57166667 0.51833333 0.58227848]
----------------------------------------
Trial 1294
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=166,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66416667 0.60916667 0.78083333 0.75083333 0.83702532]
----------------------------------------
Trial 1295
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=135, random_state=100,
subsample=0.65))])
cv score: [0.65 0.62583333 0.67916667 0.73416667 0.80775316]
----------------------------------------
Trial 1296
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=160, random_state=100))])
cv score: [0.57166667 0.70583333 0.70083333 0.8125 0.80696203]
----------------------------------------
Trial 1297
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=57, random_state=100,
subsample=0.9))])
cv score: [0.71083333 0.6275 0.70083333 0.7775 0.79825949]
----------------------------------------
Trial 1298
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=137, random_state=100,
subsample=0.65))])
cv score: [0.725 0.67 0.7925 0.835 0.83227848]
----------------------------------------
Trial 1299
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
n_estimators=78, random_state=100,
subsample=0.8))])
cv score: [0.65333333 0.6375 0.6175 0.68416667 0.6914557 ]
----------------------------------------
Trial 1300
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=164, random_state=100))])
cv score: [0.5725 0.70166667 0.70666667 0.81666667 0.81170886]
----------------------------------------
Trial 1301
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=84, random_state=100,
subsample=0.6))])
cv score: [0.59083333 0.67666667 0.67958333 0.76333333 0.76740506]
----------------------------------------
Trial 1302
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='sqrt',
n_estimators=126, random_state=100,
subsample=0.65))])
cv score: [0.66666667 0.68 0.68083333 0.84083333 0.81962025]
----------------------------------------
Trial 1303
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=192, random_state=100,
subsample=0.7))])
cv score: [0.73583333 0.71 0.75083333 0.88 0.87539557]
----------------------------------------
Trial 1304
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65083333 0.65666667 0.77416667 0.78416667 0.84493671]
----------------------------------------
Trial 1305
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=174,
random_state=100))])
cv score: [0.6925 0.50125 0.72375 0.64875 0.63251582]
----------------------------------------
Trial 1306
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=35, random_state=100,
subsample=0.95))])
cv score: [0.59583333 0.63416667 0.74833333 0.74083333 0.60838608]
----------------------------------------
Trial 1307
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=147,
random_state=100))])
cv score: [0.69583333 0.6275 0.68833333 0.79666667 0.82753165]
----------------------------------------
Trial 1308
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=88,
random_state=100))])
cv score: [0.67666667 0.60166667 0.7175 0.8075 0.81962025]
----------------------------------------
Trial 1309
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=179,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68833333 0.66166667 0.78833333 0.80666667 0.86867089]
----------------------------------------
Trial 1310
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=172,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68333333 0.65666667 0.795 0.79 0.87183544]
----------------------------------------
Trial 1311
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=41, random_state=100,
subsample=0.95))])
cv score: [0.62916667 0.65666667 0.77166667 0.7675 0.58623418]
----------------------------------------
Trial 1312
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=63, random_state=100,
subsample=0.6))])
cv score: [0.65 0.68416667 0.68916667 0.73 0.84889241]
----------------------------------------
Trial 1313
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=122, random_state=100,
subsample=0.6))])
cv score: [0.42833333 0.43875 0.5225 0.47083333 0.73496835]
----------------------------------------
Trial 1314
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67083333 0.735 0.75958333 0.85458333 0.82041139]
----------------------------------------
Trial 1315
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77 0.68916667 0.78375 0.845 0.85996835]
----------------------------------------
Trial 1316
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=161,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77416667 0.70583333 0.76416667 0.85333333 0.89477848]
----------------------------------------
Trial 1317
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='sqrt',
n_estimators=138, random_state=100,
subsample=0.95))])
cv score: [0.65333333 0.6625 0.71166667 0.81583333 0.85126582]
----------------------------------------
Trial 1318
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=117,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 1319
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=58,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72916667 0.74416667 0.77375 0.8525 0.85996835]
----------------------------------------
Trial 1320
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=197, random_state=100))])
cv score: [0.67833333 0.6625 0.71583333 0.80833333 0.85205696]
----------------------------------------
Trial 1321
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=36, random_state=100,
subsample=0.8))])
cv score: [0.71 0.66833333 0.77833333 0.83 0.82357595]
----------------------------------------
Trial 1322
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=47,
random_state=100))])
cv score: [0.55916667 0.67333333 0.69583333 0.82208333 0.84651899]
----------------------------------------
Trial 1323
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='sqrt',
n_estimators=184, random_state=100,
subsample=0.65))])
cv score: [0.49208333 0.57666667 0.64166667 0.7275 0.69382911]
----------------------------------------
Trial 1324
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=170,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70833333 0.70583333 0.73833333 0.85916667 0.86075949]
----------------------------------------
Trial 1325
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69791667 0.725 0.75125 0.83125 0.83939873]
----------------------------------------
Trial 1326
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.645 0.6675 0.78166667 0.78166667 0.78401899]
----------------------------------------
Trial 1327
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=17, random_state=100,
subsample=0.9))])
cv score: [0.71458333 0.66208333 0.775 0.85 0.83504747]
----------------------------------------
Trial 1328
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=162,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.55083333 0.625 0.75333333 0.72666667 0.77768987]
----------------------------------------
Trial 1329
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=185,
random_state=100))])
cv score: [0.56458333 0.66208333 0.7075 0.82125 0.8125 ]
----------------------------------------
Trial 1330
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=164, random_state=100))])
cv score: [0.69916667 0.62666667 0.72083333 0.81416667 0.84098101]
----------------------------------------
Trial 1331
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=167, random_state=100,
subsample=0.65))])
cv score: [0.48666667 0.58 0.64083333 0.74 0.68591772]
----------------------------------------
Trial 1332
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=187,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70833333 0.73166667 0.745 0.86666667 0.84018987]
----------------------------------------
Trial 1333
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=72,
random_state=100))])
cv score: [0.69833333 0.58166667 0.6875 0.78833333 0.79351266]
----------------------------------------
Trial 1334
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
n_estimators=139, random_state=100,
subsample=0.9))])
cv score: [0.65583333 0.66416667 0.7375 0.79 0.80379747]
----------------------------------------
Trial 1335
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=135, random_state=100))])
cv score: [0.73916667 0.68208333 0.79166667 0.8325 0.84414557]
----------------------------------------
Trial 1336
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=86,
random_state=100))])
cv score: [0.75166667 0.58791667 0.80833333 0.7925 0.80458861]
----------------------------------------
Trial 1337
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=75,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74666667 0.74416667 0.79833333 0.8675 0.86946203]
----------------------------------------
Trial 1338
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=12, random_state=100))])
cv score: [0.64875 0.685 0.66458333 0.81333333 0.82120253]
----------------------------------------
Trial 1339
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=72, random_state=100,
subsample=0.95))])
cv score: [0.62416667 0.54833333 0.7025 0.81583333 0.81566456]
----------------------------------------
Trial 1340
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=42, random_state=100,
subsample=0.85))])
cv score: [0.635 0.56916667 0.72833333 0.73916667 0.78164557]
----------------------------------------
Trial 1341
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
n_estimators=71, random_state=100,
subsample=0.7))])
cv score: [0.69416667 0.675 0.77166667 0.84583333 0.79509494]
----------------------------------------
Trial 1342
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=15, random_state=100,
subsample=0.7))])
cv score: [0.73416667 0.69 0.78083333 0.61666667 0.57041139]
----------------------------------------
Trial 1343
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=183,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59333333 0.67333333 0.77666667 0.735 0.84572785]
----------------------------------------
Trial 1344
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=180, random_state=100,
subsample=0.6))])
cv score: [0.66416667 0.4675 0.56916667 0.54416667 0.71993671]
----------------------------------------
Trial 1345
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60416667 0.67583333 0.73666667 0.6975 0.70332278]
----------------------------------------
Trial 1346
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
n_estimators=41, random_state=100,
subsample=0.6))])
cv score: [0.75916667 0.70208333 0.73833333 0.86125 0.8568038 ]
----------------------------------------
Trial 1347
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=49, random_state=100,
subsample=0.85))])
cv score: [0.75875 0.67416667 0.75166667 0.85791667 0.87539557]
----------------------------------------
Trial 1348
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71583333 0.64583333 0.7375 0.77 0.8125 ]
----------------------------------------
Trial 1349
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
n_estimators=15, random_state=100,
subsample=0.7))])
cv score: [0.57458333 0.68875 0.81041667 0.68541667 0.69066456]
----------------------------------------
Trial 1350
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64666667 0.6375 0.76833333 0.735 0.81566456]
----------------------------------------
Trial 1351
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=69, random_state=100))])
cv score: [0.65833333 0.64916667 0.7425 0.82208333 0.82278481]
----------------------------------------
Trial 1352
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=198,
random_state=100))])
cv score: [0.67333333 0.61416667 0.675 0.78333333 0.75553797]
----------------------------------------
Trial 1353
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='log2',
n_estimators=134, random_state=100,
subsample=0.8))])
cv score: [0.5275 0.55666667 0.62 0.71833333 0.64398734]
----------------------------------------
Trial 1354
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=120,
random_state=100))])
cv score: [0.63875 0.57916667 0.7425 0.78375 0.78085443]
----------------------------------------
Trial 1355
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=159, random_state=100))])
cv score: [0.69416667 0.6525 0.715 0.79833333 0.83702532]
----------------------------------------
Trial 1356
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='sqrt',
n_estimators=56, random_state=100,
subsample=0.9))])
cv score: [0.55625 0.62375 0.68625 0.835 0.7721519]
----------------------------------------
Trial 1357
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.85))])
cv score: [0.53833333 0.585 0.64166667 0.6775 0.78639241]
----------------------------------------
Trial 1358
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
n_estimators=143, random_state=100,
subsample=0.65))])
cv score: [0.6775 0.6925 0.70416667 0.77333333 0.7318038 ]
----------------------------------------
Trial 1359
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='log2',
n_estimators=145, random_state=100,
subsample=0.65))])
cv score: [0.605 0.5925 0.6975 0.76916667 0.79667722]
----------------------------------------
Trial 1360
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.675 0.7225 0.7375 0.8575 0.84731013]
----------------------------------------
Trial 1361
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=184, random_state=100,
subsample=0.65))])
cv score: [0.5725 0.58833333 0.53583333 0.64333333 0.65348101]
----------------------------------------
Trial 1362
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=38,
random_state=100))])
cv score: [0.765 0.49375 0.78875 0.69958333 0.79232595]
----------------------------------------
Trial 1363
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=20, random_state=100,
subsample=0.8))])
cv score: [0.67333333 0.63 0.65333333 0.5825 0.55142405]
----------------------------------------
Trial 1364
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72833333 0.71416667 0.77583333 0.8325 0.9153481 ]
----------------------------------------
Trial 1365
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=167, random_state=100,
subsample=0.8))])
cv score: [0.56583333 0.655 0.69333333 0.65916667 0.70332278]
----------------------------------------
Trial 1366
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='log2',
n_estimators=64, random_state=100,
subsample=0.8))])
cv score: [0.6975 0.62 0.72 0.8175 0.82594937]
----------------------------------------
Trial 1367
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=39,
random_state=100))])
cv score: [0.68541667 0.49375 0.80833333 0.64583333 0.75237342]
----------------------------------------
Trial 1368
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=174,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69333333 0.66666667 0.79333333 0.78 0.8568038 ]
----------------------------------------
Trial 1369
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.7))])
cv score: [0.535 0.57583333 0.58 0.57333333 0.60522152]
----------------------------------------
Trial 1370
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=147, random_state=100,
subsample=0.8))])
cv score: [0.7425 0.66583333 0.80083333 0.835 0.82120253]
----------------------------------------
Trial 1371
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57 0.62 0.75833333 0.70083333 0.80458861]
----------------------------------------
Trial 1372
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=109, random_state=100))])
cv score: [0.7775 0.6825 0.75333333 0.85333333 0.87025316]
----------------------------------------
Trial 1373
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=77, random_state=100))])
cv score: [0.60166667 0.68583333 0.70583333 0.78 0.82515823]
----------------------------------------
Trial 1374
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71083333 0.74666667 0.77208333 0.85916667 0.87341772]
----------------------------------------
Trial 1375
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=96, random_state=100,
subsample=0.85))])
cv score: [0.66 0.64583333 0.6875 0.79833333 0.81724684]
----------------------------------------
Trial 1376
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='sqrt',
n_estimators=17, random_state=100,
subsample=0.95))])
cv score: [0.60166667 0.575 0.68833333 0.765 0.72943038]
----------------------------------------
Trial 1377
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=181,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75916667 0.71916667 0.79833333 0.85083333 0.88053797]
----------------------------------------
Trial 1378
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=23, random_state=100))])
cv score: [0.74916667 0.64041667 0.6875 0.70541667 0.875 ]
----------------------------------------
Trial 1379
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=83, random_state=100))])
cv score: [0.6575 0.62916667 0.75 0.7975 0.81724684]
----------------------------------------
Trial 1380
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=75, random_state=100))])
cv score: [0.57333333 0.72791667 0.69083333 0.79166667 0.80458861]
----------------------------------------
Trial 1381
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=84, random_state=100,
subsample=0.65))])
cv score: [0.59916667 0.6125 0.705 0.79666667 0.78481013]
----------------------------------------
Trial 1382
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=106,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.69666667 0.77583333 0.80083333 0.88132911]
----------------------------------------
Trial 1383
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=163, random_state=100))])
cv score: [0.77666667 0.68 0.74166667 0.85 0.89873418]
----------------------------------------
Trial 1384
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=97, random_state=100,
subsample=0.65))])
cv score: [0.66833333 0.62666667 0.67833333 0.8125 0.79905063]
----------------------------------------
Trial 1385
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=14, random_state=100))])
cv score: [0.71041667 0.63166667 0.76083333 0.86458333 0.80261076]
----------------------------------------
Trial 1386
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
n_estimators=54, random_state=100,
subsample=0.65))])
cv score: [0.71083333 0.55375 0.6075 0.4725 0.47547468]
----------------------------------------
Trial 1387
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59166667 0.59833333 0.72 0.6975 0.76107595]
----------------------------------------
Trial 1388
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76583333 0.72916667 0.77 0.855 0.87183544]
----------------------------------------
Trial 1389
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77666667 0.7275 0.78083333 0.83916667 0.8931962 ]
----------------------------------------
Trial 1390
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=199, random_state=100))])
cv score: [0.665 0.66583333 0.70333333 0.82333333 0.85917722]
----------------------------------------
Trial 1391
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=76,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 1392
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=162, random_state=100,
subsample=0.7))])
cv score: [0.70333333 0.50166667 0.47166667 0.47166667 0.5585443 ]
----------------------------------------
Trial 1393
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.75))])
cv score: [0.59583333 0.71 0.6775 0.81333333 0.80300633]
----------------------------------------
Trial 1394
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=16,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69666667 0.69416667 0.73333333 0.76416667 0.82120253]
----------------------------------------
Trial 1395
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.7))])
cv score: [0.68083333 0.64 0.71 0.8025 0.81882911]
----------------------------------------
Trial 1396
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=127,
random_state=100))])
cv score: [0.69291667 0.4875 0.72083333 0.6675 0.63568038]
----------------------------------------
Trial 1397
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73791667 0.75291667 0.7825 0.8625 0.84968354]
----------------------------------------
Trial 1398
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=94, random_state=100))])
cv score: [0.70583333 0.62 0.72416667 0.79583333 0.83623418]
----------------------------------------
Trial 1399
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=129, random_state=100))])
cv score: [0.665 0.64083333 0.725 0.815 0.84098101]
----------------------------------------
Trial 1400
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.72458333 0.77708333 0.83666667 0.86867089]
----------------------------------------
Trial 1401
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.75))])
cv score: [0.61083333 0.59833333 0.72166667 0.6625 0.66772152]
----------------------------------------
Trial 1402
----------------------------------------
Parameters {'xgb__n_estimators': 115, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=115,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75833333 0.73625 0.78291667 0.86666667 0.84810127]
----------------------------------------
Trial 1403
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7525 0.7275 0.81 0.79666667 0.88528481]
----------------------------------------
Trial 1404
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=19, random_state=100))])
cv score: [0.71041667 0.65 0.75791667 0.87291667 0.8255538 ]
----------------------------------------
Trial 1405
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.7 0.77666667 0.81541667 0.88844937]
----------------------------------------
Trial 1406
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=103,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67583333 0.62833333 0.72583333 0.7825 0.74841772]
----------------------------------------
Trial 1407
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=63, random_state=100))])
cv score: [0.65916667 0.59916667 0.71666667 0.775 0.83860759]
----------------------------------------
Trial 1408
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=110, random_state=100))])
cv score: [0.7175 0.68166667 0.78083333 0.83666667 0.83939873]
----------------------------------------
Trial 1409
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=39, random_state=100))])
cv score: [0.67583333 0.69 0.725 0.76833333 0.84177215]
----------------------------------------
Trial 1410
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=171,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 1411
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='sqrt',
n_estimators=54, random_state=100,
subsample=0.6))])
cv score: [0.70916667 0.6675 0.70916667 0.835 0.8164557 ]
----------------------------------------
Trial 1412
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=127, random_state=100))])
cv score: [0.6775 0.63083333 0.75791667 0.82916667 0.82990506]
----------------------------------------
Trial 1413
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=40, random_state=100,
subsample=0.9))])
cv score: [0.69916667 0.61166667 0.64166667 0.80583333 0.73892405]
----------------------------------------
Trial 1414
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69708333 0.75291667 0.76208333 0.83291667 0.87223101]
----------------------------------------
Trial 1415
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=106, random_state=100))])
cv score: [0.7825 0.68333333 0.75666667 0.85333333 0.86787975]
----------------------------------------
Trial 1416
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=73,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83625 0.82199367]
----------------------------------------
Trial 1417
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='log2',
n_estimators=73,
random_state=100))])
cv score: [0.55583333 0.63958333 0.69541667 0.80333333 0.74367089]
----------------------------------------
Trial 1418
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=41,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63 0.66833333 0.715 0.77333333 0.79272152]
----------------------------------------
Trial 1419
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=76,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75625 0.73625 0.7875 0.86416667 0.92325949]
----------------------------------------
Trial 1420
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62625 0.73291667 0.76041667 0.85375 0.84968354]
----------------------------------------
Trial 1421
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=157,
random_state=100))])
cv score: [0.68708333 0.49375 0.80833333 0.64666667 0.75316456]
----------------------------------------
Trial 1422
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=44, random_state=100))])
cv score: [0.7475 0.65958333 0.77416667 0.83833333 0.85759494]
----------------------------------------
Trial 1423
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='log2',
n_estimators=83, random_state=100,
subsample=0.9))])
cv score: [0.53 0.63666667 0.67875 0.80208333 0.75276899]
----------------------------------------
Trial 1424
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=142,
random_state=100))])
cv score: [0.68916667 0.59666667 0.69666667 0.79 0.80933544]
----------------------------------------
Trial 1425
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=67, random_state=100))])
cv score: [0.68416667 0.61 0.7225 0.7875 0.83148734]
----------------------------------------
Trial 1426
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=102,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60083333 0.67083333 0.77583333 0.76416667 0.78401899]
----------------------------------------
Trial 1427
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=124,
random_state=100))])
cv score: [0.56291667 0.65875 0.68833333 0.83208333 0.80023734]
----------------------------------------
Trial 1428
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=70, random_state=100,
subsample=0.9))])
cv score: [0.62916667 0.56916667 0.70583333 0.74666667 0.77927215]
----------------------------------------
Trial 1429
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=21, random_state=100))])
cv score: [0.65208333 0.61541667 0.70666667 0.74458333 0.7721519 ]
----------------------------------------
Trial 1430
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=29, random_state=100))])
cv score: [0.62875 0.67 0.65875 0.81333333 0.79469937]
----------------------------------------
Trial 1431
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=119, random_state=100))])
cv score: [0.6925 0.62583333 0.74166667 0.7975 0.82990506]
----------------------------------------
Trial 1432
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
n_estimators=198, random_state=100,
subsample=0.7))])
cv score: [0.77916667 0.67416667 0.7725 0.85833333 0.88607595]
----------------------------------------
Trial 1433
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='sqrt',
n_estimators=26, random_state=100,
subsample=0.9))])
cv score: [0.7 0.535 0.67666667 0.74916667 0.81803797]
----------------------------------------
Trial 1434
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=197,
random_state=100))])
cv score: [0.65333333 0.66916667 0.69833333 0.82333333 0.83939873]
----------------------------------------
Trial 1435
----------------------------------------
Parameters {'xgb__n_estimators': 56, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=56,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6675 0.64083333 0.72333333 0.7175 0.78401899]
----------------------------------------
Trial 1436
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=135,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57333333 0.615 0.74583333 0.73416667 0.72072785]
----------------------------------------
Trial 1437
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=129, random_state=100))])
cv score: [0.74666667 0.68333333 0.78666667 0.8325 0.84968354]
----------------------------------------
Trial 1438
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
n_estimators=61, random_state=100,
subsample=0.7))])
cv score: [0.72083333 0.66833333 0.70333333 0.71666667 0.68908228]
----------------------------------------
Trial 1439
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=150, random_state=100,
subsample=0.8))])
cv score: [0.7275 0.6275 0.59916667 0.69333333 0.70490506]
----------------------------------------
Trial 1440
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
n_estimators=27,
random_state=100))])
cv score: [0.53375 0.545 0.57833333 0.71333333 0.6028481 ]
----------------------------------------
Trial 1441
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=25, random_state=100,
subsample=0.7))])
cv score: [0.71416667 0.57416667 0.74833333 0.6475 0.61946203]
----------------------------------------
Trial 1442
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
n_estimators=134, random_state=100,
subsample=0.85))])
cv score: [0.7375 0.66458333 0.81166667 0.8275 0.81724684]
----------------------------------------
Trial 1443
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=32, random_state=100,
subsample=0.8))])
cv score: [0.72416667 0.63833333 0.71166667 0.82333333 0.91060127]
----------------------------------------
Trial 1444
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=116, random_state=100))])
cv score: [0.5625 0.71916667 0.7025 0.78833333 0.79746835]
----------------------------------------
Trial 1445
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=196,
random_state=100))])
cv score: [0.63083333 0.57791667 0.72833333 0.79416667 0.80221519]
----------------------------------------
Trial 1446
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=101,
random_state=100))])
cv score: [0.65 0.58 0.69916667 0.815 0.8164557 ]
----------------------------------------
Trial 1447
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=36,
random_state=100))])
cv score: [0.75583333 0.58791667 0.81083333 0.78541667 0.80379747]
----------------------------------------
Trial 1448
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7475 0.7525 0.78583333 0.85166667 0.87341772]
----------------------------------------
Trial 1449
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.6))])
cv score: [0.57083333 0.63166667 0.7275 0.74416667 0.80537975]
----------------------------------------
Trial 1450
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='sqrt',
n_estimators=70, random_state=100,
subsample=0.65))])
cv score: [0.61916667 0.4875 0.71833333 0.7325 0.73813291]
----------------------------------------
Trial 1451
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=186,
random_state=100,
subsample=0.65))])
cv score: [0.64333333 0.6225 0.68583333 0.74166667 0.43987342]
----------------------------------------
Trial 1452
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.75))])
cv score: [0.64 0.67083333 0.7075 0.82333333 0.84493671]
----------------------------------------
Trial 1453
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='sqrt',
n_estimators=192, random_state=100,
subsample=0.8))])
cv score: [0.61333333 0.59833333 0.6325 0.72666667 0.68433544]
----------------------------------------
Trial 1454
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=181, random_state=100,
subsample=0.65))])
cv score: [0.68083333 0.65166667 0.64333333 0.77 0.78164557]
----------------------------------------
Trial 1455
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=76, random_state=100))])
cv score: [0.61875 0.68916667 0.64333333 0.82916667 0.80379747]
----------------------------------------
Trial 1456
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77333333 0.72625 0.79625 0.84125 0.92484177]
----------------------------------------
Trial 1457
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=184,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60583333 0.625 0.78916667 0.735 0.84968354]
----------------------------------------
Trial 1458
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=168,
random_state=100))])
cv score: [0.645 0.57875 0.73208333 0.78291667 0.79113924]
----------------------------------------
Trial 1459
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=68,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.73083333 0.785 0.85 0.89873418]
----------------------------------------
Trial 1460
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=117, random_state=100,
subsample=0.8))])
cv score: [0.72583333 0.6675 0.80833333 0.84166667 0.80933544]
----------------------------------------
Trial 1461
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=194, random_state=100,
subsample=0.8))])
cv score: [0.65333333 0.70833333 0.58083333 0.61583333 0.52136076]
----------------------------------------
Trial 1462
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=195, random_state=100))])
cv score: [0.5825 0.70166667 0.70666667 0.81916667 0.8164557 ]
----------------------------------------
Trial 1463
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=151,
random_state=100))])
cv score: [0.63166667 0.6825 0.7225 0.8375 0.83939873]
----------------------------------------
Trial 1464
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=84,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74083333 0.68333333 0.785 0.80833333 0.85917722]
----------------------------------------
Trial 1465
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=61, random_state=100,
subsample=0.75))])
cv score: [0.6125 0.565 0.67666667 0.79666667 0.66218354]
----------------------------------------
Trial 1466
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=186,
random_state=100))])
cv score: [0.69416667 0.51791667 0.7975 0.66916667 0.62816456]
----------------------------------------
Trial 1467
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=183,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75916667 0.71375 0.78416667 0.84833333 0.90981013]
----------------------------------------
Trial 1468
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='sqrt',
n_estimators=28, random_state=100,
subsample=0.6))])
cv score: [0.72166667 0.62916667 0.6825 0.83166667 0.79905063]
----------------------------------------
Trial 1469
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68583333 0.63833333 0.79583333 0.76333333 0.82199367]
----------------------------------------
Trial 1470
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='log2',
n_estimators=169, random_state=100,
subsample=0.6))])
cv score: [0.635 0.61833333 0.61333333 0.66416667 0.6914557 ]
----------------------------------------
Trial 1471
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74666667 0.69333333 0.81583333 0.82333333 0.86787975]
----------------------------------------
Trial 1472
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.72583333 0.75208333 0.87166667 0.85759494]
----------------------------------------
Trial 1473
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68166667 0.66333333 0.79 0.80166667 0.86234177]
----------------------------------------
Trial 1474
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
n_estimators=57, random_state=100,
subsample=0.95))])
cv score: [0.81625 0.67333333 0.76458333 0.86083333 0.83781646]
----------------------------------------
Trial 1475
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=17,
random_state=100))])
cv score: [0.57583333 0.66625 0.68 0.835 0.80498418]
----------------------------------------
Trial 1476
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=176,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74166667 0.7525 0.78 0.87583333 0.8568038 ]
----------------------------------------
Trial 1477
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=127, random_state=100,
subsample=0.9))])
cv score: [0.59166667 0.60083333 0.67583333 0.6875 0.72547468]
----------------------------------------
Trial 1478
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=50, random_state=100,
subsample=0.65))])
cv score: [0.5675 0.53 0.605 0.62833333 0.65822785]
----------------------------------------
Trial 1479
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=110, random_state=100,
subsample=0.85))])
cv score: [0.54083333 0.58833333 0.64416667 0.66333333 0.78955696]
----------------------------------------
Trial 1480
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=119,
random_state=100))])
cv score: [0.695 0.63916667 0.6975 0.80333333 0.82515823]
----------------------------------------
Trial 1481
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=158, random_state=100,
subsample=0.7))])
cv score: [0.70666667 0.6625 0.77333333 0.82916667 0.83702532]
----------------------------------------
Trial 1482
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=15,
random_state=100))])
cv score: [0.74333333 0.62708333 0.66833333 0.73041667 0.72231013]
----------------------------------------
Trial 1483
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=25,
random_state=100))])
cv score: [0.7675 0.52041667 0.75625 0.70416667 0.6835443 ]
----------------------------------------
Trial 1484
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=191,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.775 0.735 0.79 0.83333333 0.87420886]
----------------------------------------
Trial 1485
----------------------------------------
Parameters {'xgb__n_estimators': 63, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=63,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66 0.68 0.7725 0.79916667 0.88686709]
----------------------------------------
Trial 1486
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=139, random_state=100))])
cv score: [0.67916667 0.64333333 0.72416667 0.8125 0.83860759]
----------------------------------------
Trial 1487
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=43,
random_state=100))])
cv score: [0.76708333 0.49375 0.78791667 0.70125 0.79232595]
----------------------------------------
Trial 1488
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67083333 0.64333333 0.75666667 0.67833333 0.76186709]
----------------------------------------
Trial 1489
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=107, random_state=100,
subsample=0.95))])
cv score: [0.43583333 0.55666667 0.65083333 0.695 0.69541139]
----------------------------------------
Trial 1490
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=147, random_state=100,
subsample=0.6))])
cv score: [0.635 0.66583333 0.70083333 0.80416667 0.81329114]
----------------------------------------
Trial 1491
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='log2',
n_estimators=75, random_state=100,
subsample=0.9))])
cv score: [0.6625 0.59 0.66666667 0.7025 0.71360759]
----------------------------------------
Trial 1492
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=135, random_state=100,
subsample=0.95))])
cv score: [0.64333333 0.68833333 0.73583333 0.80666667 0.80696203]
----------------------------------------
Trial 1493
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=184, random_state=100,
subsample=0.6))])
cv score: [0.68 0.615 0.72083333 0.82 0.82436709]
----------------------------------------
Trial 1494
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68166667 0.63333333 0.76416667 0.74583333 0.79667722]
----------------------------------------
Trial 1495
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62333333 0.64416667 0.74833333 0.7 0.80537975]
----------------------------------------
Trial 1496
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=108, random_state=100,
subsample=0.9))])
cv score: [0.55 0.61666667 0.70666667 0.6975 0.74683544]
----------------------------------------
Trial 1497
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=126, random_state=100,
subsample=0.95))])
cv score: [0.635 0.575 0.66166667 0.77083333 0.77056962]
----------------------------------------
Trial 1498
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=33,
random_state=100))])
cv score: [0.69041667 0.48916667 0.77958333 0.67125 0.63924051]
----------------------------------------
Trial 1499
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=14, random_state=100,
subsample=0.6))])
cv score: [0.74416667 0.68833333 0.74333333 0.78333333 0.80458861]
----------------------------------------
Trial 1500
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=182, random_state=100,
subsample=0.85))])
cv score: [0.47666667 0.61 0.63 0.74166667 0.69778481]
----------------------------------------
Trial 1501
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=153,
random_state=100))])
cv score: [0.685 0.60166667 0.70166667 0.7925 0.81408228]
----------------------------------------
Trial 1502
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=179, random_state=100))])
cv score: [0.74166667 0.67583333 0.77833333 0.83666667 0.84731013]
----------------------------------------
Trial 1503
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=142,
random_state=100))])
cv score: [0.56208333 0.66041667 0.69083333 0.82625 0.80537975]
----------------------------------------
Trial 1504
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=119, random_state=100,
subsample=0.8))])
cv score: [0.675 0.60416667 0.73166667 0.77916667 0.70806962]
----------------------------------------
Trial 1505
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=42,
random_state=100))])
cv score: [0.6475 0.65375 0.69166667 0.80958333 0.83781646]
----------------------------------------
Trial 1506
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=102,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71666667 0.65333333 0.74666667 0.77833333 0.85601266]
----------------------------------------
Trial 1507
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=117,
random_state=100))])
cv score: [0.69333333 0.60083333 0.67583333 0.80583333 0.83544304]
----------------------------------------
Trial 1508
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=129,
random_state=100))])
cv score: [0.66333333 0.5925 0.72208333 0.79125 0.77096519]
----------------------------------------
Trial 1509
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=59,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79708333 0.73958333 0.76833333 0.83583333 0.8710443 ]
----------------------------------------
Trial 1510
----------------------------------------
Parameters {'gb__n_estimators': 55, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=55, random_state=100,
subsample=0.9))])
cv score: [0.65833333 0.6175 0.72416667 0.795 0.82753165]
----------------------------------------
Trial 1511
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70708333 0.72833333 0.75041667 0.865 0.85601266]
----------------------------------------
Trial 1512
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=61, random_state=100,
subsample=0.7))])
cv score: [0.5425 0.65 0.7125 0.6075 0.75316456]
----------------------------------------
Trial 1513
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=120,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83708333 0.82199367]
----------------------------------------
Trial 1514
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=16,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64833333 0.63583333 0.74 0.70416667 0.77768987]
----------------------------------------
Trial 1515
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='sqrt',
n_estimators=126, random_state=100,
subsample=0.7))])
cv score: [0.65916667 0.62416667 0.7275 0.80583333 0.80775316]
----------------------------------------
Trial 1516
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.68 0.78833333 0.815 0.89398734]
----------------------------------------
Trial 1517
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.65))])
cv score: [0.62 0.52666667 0.56916667 0.63583333 0.67879747]
----------------------------------------
Trial 1518
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=189, random_state=100))])
cv score: [0.62291667 0.68708333 0.65208333 0.8175 0.82792722]
----------------------------------------
Trial 1519
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=96,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71666667 0.73333333 0.75958333 0.86666667 0.85126582]
----------------------------------------
Trial 1520
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=21,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83625 0.82199367]
----------------------------------------
Trial 1521
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=68,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64 0.62666667 0.72833333 0.73166667 0.71677215]
----------------------------------------
Trial 1522
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=24, random_state=100,
subsample=0.9))])
cv score: [0.7325 0.63166667 0.73416667 0.67916667 0.69303797]
----------------------------------------
Trial 1523
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=86, random_state=100,
subsample=0.85))])
cv score: [0.6 0.54333333 0.635 0.66 0.75712025]
----------------------------------------
Trial 1524
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7575 0.71166667 0.76958333 0.85166667 0.87262658]
----------------------------------------
Trial 1525
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.65166667 0.74666667 0.77 0.78322785]
----------------------------------------
Trial 1526
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=187, random_state=100,
subsample=0.65))])
cv score: [0.66333333 0.5875 0.66166667 0.71666667 0.74841772]
----------------------------------------
Trial 1527
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=37, random_state=100))])
cv score: [0.62333333 0.68916667 0.71875 0.8125 0.84889241]
----------------------------------------
Trial 1528
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
n_estimators=80, random_state=100,
subsample=0.85))])
cv score: [0.73 0.66 0.76666667 0.825 0.80221519]
----------------------------------------
Trial 1529
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
n_estimators=193, random_state=100,
subsample=0.95))])
cv score: [0.67166667 0.60666667 0.75833333 0.80583333 0.78085443]
----------------------------------------
Trial 1530
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='sqrt',
n_estimators=64, random_state=100,
subsample=0.7))])
cv score: [0.63416667 0.60166667 0.695 0.75916667 0.72310127]
----------------------------------------
Trial 1531
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=26, random_state=100))])
cv score: [0.6825 0.63 0.70416667 0.79166667 0.88686709]
----------------------------------------
Trial 1532
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=147, random_state=100,
subsample=0.8))])
cv score: [0.62833333 0.605 0.69166667 0.78916667 0.81487342]
----------------------------------------
Trial 1533
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=59, random_state=100))])
cv score: [0.7125 0.68833333 0.71666667 0.84666667 0.87816456]
----------------------------------------
Trial 1534
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=29, random_state=100))])
cv score: [0.64666667 0.65333333 0.71916667 0.77666667 0.81408228]
----------------------------------------
Trial 1535
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=147,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7575 0.75333333 0.78958333 0.86666667 0.85126582]
----------------------------------------
Trial 1536
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=163,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64833333 0.65583333 0.78166667 0.79166667 0.86787975]
----------------------------------------
Trial 1537
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='sqrt',
n_estimators=97, random_state=100,
subsample=0.7))])
cv score: [0.62083333 0.63083333 0.64583333 0.75333333 0.74208861]
----------------------------------------
Trial 1538
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73833333 0.69833333 0.81333333 0.82083333 0.90585443]
----------------------------------------
Trial 1539
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
n_estimators=130, random_state=100,
subsample=0.65))])
cv score: [0.63833333 0.66833333 0.7225 0.81166667 0.71202532]
----------------------------------------
Trial 1540
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=20, random_state=100))])
cv score: [0.65625 0.58625 0.70833333 0.73541667 0.75593354]
----------------------------------------
Trial 1541
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=172, random_state=100,
subsample=0.65))])
cv score: [0.6325 0.60083333 0.67416667 0.72083333 0.69382911]
----------------------------------------
Trial 1542
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=108, random_state=100))])
cv score: [0.65666667 0.68 0.74833333 0.78 0.84414557]
----------------------------------------
Trial 1543
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=135, random_state=100))])
cv score: [0.75666667 0.67958333 0.7875 0.8325 0.84731013]
----------------------------------------
Trial 1544
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=198,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66416667 0.63375 0.7775 0.675 0.75 ]
----------------------------------------
Trial 1545
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56666667 0.66166667 0.78333333 0.72083333 0.79193038]
----------------------------------------
Trial 1546
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=163,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79333333 0.7225 0.80458333 0.84333333 0.84335443]
----------------------------------------
Trial 1547
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=24, random_state=100,
subsample=0.95))])
cv score: [0.6575 0.64291667 0.73416667 0.80916667 0.77848101]
----------------------------------------
Trial 1548
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=181, random_state=100))])
cv score: [0.69666667 0.64166667 0.70916667 0.80166667 0.83939873]
----------------------------------------
Trial 1549
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69833333 0.67 0.79666667 0.7775 0.86629747]
----------------------------------------
Trial 1550
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=132,
random_state=100))])
cv score: [0.645 0.57791667 0.73916667 0.78875 0.77610759]
----------------------------------------
Trial 1551
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=176,
random_state=100))])
cv score: [0.6825 0.5875 0.69833333 0.80166667 0.81170886]
----------------------------------------
Trial 1552
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=64,
random_state=100))])
cv score: [0.62458333 0.59416667 0.71083333 0.78291667 0.78243671]
----------------------------------------
Trial 1553
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=127, random_state=100))])
cv score: [0.6775 0.63083333 0.75791667 0.82916667 0.82990506]
----------------------------------------
Trial 1554
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66208333 0.7425 0.75541667 0.85041667 0.88132911]
----------------------------------------
Trial 1555
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=169, random_state=100,
subsample=0.75))])
cv score: [0.47333333 0.60416667 0.68833333 0.66666667 0.71281646]
----------------------------------------
Trial 1556
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73166667 0.65833333 0.7775 0.77083333 0.87420886]
----------------------------------------
Trial 1557
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=171,
random_state=100, subsample=0.7))])
cv score: [0.7475 0.63791667 0.6525 0.78 0.68987342]
----------------------------------------
Trial 1558
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=103, random_state=100,
subsample=0.7))])
cv score: [0.65583333 0.6175 0.7125 0.80833333 0.7721519 ]
----------------------------------------
Trial 1559
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=170,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.66833333 0.79416667 0.76666667 0.84889241]
----------------------------------------
Trial 1560
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 1561
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=180,
random_state=100))])
cv score: [0.71333333 0.6 0.67166667 0.81083333 0.84414557]
----------------------------------------
Trial 1562
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=73, random_state=100))])
cv score: [0.66416667 0.64916667 0.74416667 0.81333333 0.82357595]
----------------------------------------
Trial 1563
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=170, random_state=100,
subsample=0.8))])
cv score: [0.42333333 0.65333333 0.70833333 0.5125 0.47626582]
----------------------------------------
Trial 1564
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.74666667 0.79916667 0.87083333 0.87974684]
----------------------------------------
Trial 1565
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7925 0.72666667 0.76791667 0.85166667 0.88132911]
----------------------------------------
Trial 1566
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73666667 0.75125 0.77791667 0.85833333 0.85996835]
----------------------------------------
Trial 1567
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70291667 0.7475 0.73708333 0.87 0.84018987]
----------------------------------------
Trial 1568
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=171, random_state=100))])
cv score: [0.5725 0.70333333 0.70166667 0.81 0.81803797]
----------------------------------------
Trial 1569
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57958333 0.72583333 0.7425 0.84916667 0.80735759]
----------------------------------------
Trial 1570
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='log2',
n_estimators=89, random_state=100,
subsample=0.8))])
cv score: [0.62083333 0.5575 0.7025 0.73333333 0.6693038 ]
----------------------------------------
Trial 1571
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=176,
random_state=100))])
cv score: [0.6725 0.62416667 0.70333333 0.79833333 0.83227848]
----------------------------------------
Trial 1572
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=95,
random_state=100))])
cv score: [0.6825 0.6025 0.72166667 0.80916667 0.82199367]
----------------------------------------
Trial 1573
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=132,
random_state=100))])
cv score: [0.67916667 0.58666667 0.69666667 0.79166667 0.80221519]
----------------------------------------
Trial 1574
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, n_estimators=83,
random_state=100))])
cv score: [0.70916667 0.64 0.74541667 0.79 0.84889241]
----------------------------------------
Trial 1575
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63083333 0.64 0.76333333 0.80166667 0.85126582]
----------------------------------------
Trial 1576
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='sqrt',
n_estimators=146,
random_state=100))])
cv score: [0.59833333 0.64083333 0.68583333 0.72583333 0.6028481 ]
----------------------------------------
Trial 1577
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=44,
random_state=100))])
cv score: [0.66083333 0.68875 0.67833333 0.80333333 0.83702532]
----------------------------------------
Trial 1578
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=34,
random_state=100))])
cv score: [0.65625 0.57541667 0.66291667 0.78166667 0.83267405]
----------------------------------------
Trial 1579
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=25,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67875 0.74833333 0.755 0.83666667 0.86352848]
----------------------------------------
Trial 1580
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='log2',
n_estimators=122, random_state=100,
subsample=0.95))])
cv score: [0.60166667 0.65875 0.69083333 0.80958333 0.78164557]
----------------------------------------
Trial 1581
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=107,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7125 0.67833333 0.7725 0.79583333 0.88607595]
----------------------------------------
Trial 1582
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='sqrt',
n_estimators=165, random_state=100,
subsample=0.75))])
cv score: [0.605 0.5775 0.65416667 0.67 0.65506329]
----------------------------------------
Trial 1583
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=197, random_state=100,
subsample=0.7))])
cv score: [0.60583333 0.67583333 0.69 0.475 0.58109177]
----------------------------------------
Trial 1584
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=171, random_state=100,
subsample=0.75))])
cv score: [0.70083333 0.64666667 0.69916667 0.81083333 0.83306962]
----------------------------------------
Trial 1585
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=104,
random_state=100))])
cv score: [0.695 0.51791667 0.8 0.67 0.62856013]
----------------------------------------
Trial 1586
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=158,
random_state=100))])
cv score: [0.62833333 0.4425 0.73125 0.62666667 0.67088608]
----------------------------------------
Trial 1587
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=179,
random_state=100))])
cv score: [0.685 0.63083333 0.68416667 0.7925 0.81566456]
----------------------------------------
Trial 1588
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=177,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59166667 0.71291667 0.73041667 0.85333333 0.82318038]
----------------------------------------
Trial 1589
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=115, random_state=100))])
cv score: [0.71083333 0.60541667 0.73791667 0.81166667 0.79588608]
----------------------------------------
Trial 1590
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.75166667 0.78666667 0.85666667 0.85047468]
----------------------------------------
Trial 1591
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=71, random_state=100,
subsample=0.95))])
cv score: [0.65833333 0.58333333 0.70833333 0.76666667 0.74841772]
----------------------------------------
Trial 1592
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72833333 0.72541667 0.77625 0.83666667 0.86787975]
----------------------------------------
Trial 1593
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=106,
random_state=100))])
cv score: [0.73583333 0.59125 0.76 0.76 0.69343354]
----------------------------------------
Trial 1594
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
n_estimators=179, random_state=100,
subsample=0.8))])
cv score: [0.68916667 0.61333333 0.74166667 0.75083333 0.72863924]
----------------------------------------
Trial 1595
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58583333 0.60666667 0.7125 0.64166667 0.75632911]
----------------------------------------
Trial 1596
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=92,
random_state=100, subsample=0.8))])
cv score: [0.67666667 0.67416667 0.7625 0.82666667 0.79113924]
----------------------------------------
Trial 1597
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='sqrt',
n_estimators=97, random_state=100,
subsample=0.8))])
cv score: [0.67916667 0.66416667 0.48083333 0.62083333 0.51028481]
----------------------------------------
Trial 1598
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=126, random_state=100))])
cv score: [0.68916667 0.59916667 0.74333333 0.82166667 0.82792722]
----------------------------------------
Trial 1599
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.75))])
cv score: [0.69333333 0.65166667 0.70666667 0.815 0.83227848]
----------------------------------------
Trial 1600
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61625 0.74583333 0.73916667 0.8325 0.82832278]
----------------------------------------
Trial 1601
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='log2',
n_estimators=195, random_state=100,
subsample=0.7))])
cv score: [0.62833333 0.58583333 0.64916667 0.71833333 0.71044304]
----------------------------------------
Trial 1602
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=99,
random_state=100))])
cv score: [0.63083333 0.59458333 0.73666667 0.78166667 0.75158228]
----------------------------------------
Trial 1603
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69333333 0.70916667 0.79833333 0.80416667 0.91139241]
----------------------------------------
Trial 1604
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=16, random_state=100))])
cv score: [0.59166667 0.5675 0.715 0.7375 0.83030063]
----------------------------------------
Trial 1605
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.62583333 0.73083333 0.7525 0.82515823]
----------------------------------------
Trial 1606
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=103,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66666667 0.65583333 0.79666667 0.79166667 0.84572785]
----------------------------------------
Trial 1607
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=144, random_state=100,
subsample=0.65))])
cv score: [0.72333333 0.69833333 0.73333333 0.80833333 0.82515823]
----------------------------------------
Trial 1608
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=92,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65 0.61916667 0.74083333 0.77666667 0.79667722]
----------------------------------------
Trial 1609
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=161,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7575 0.71583333 0.77041667 0.85583333 0.85640823]
----------------------------------------
Trial 1610
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73 0.57833333 0.75 0.77333333 0.82753165]
----------------------------------------
Trial 1611
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.72541667 0.77958333 0.86375 0.86550633]
----------------------------------------
Trial 1612
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=161,
random_state=100))])
cv score: [0.6925 0.50125 0.72375 0.64875 0.63370253]
----------------------------------------
Trial 1613
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=105, random_state=100))])
cv score: [0.6575 0.68083333 0.74083333 0.7825 0.83860759]
----------------------------------------
Trial 1614
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=148, random_state=100,
subsample=0.95))])
cv score: [0.755 0.63791667 0.81666667 0.835 0.79667722]
----------------------------------------
Trial 1615
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=193, random_state=100))])
cv score: [0.60083333 0.68583333 0.70666667 0.81166667 0.82990506]
----------------------------------------
Trial 1616
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=124,
random_state=100))])
cv score: [0.76458333 0.49375 0.78791667 0.70208333 0.79232595]
----------------------------------------
Trial 1617
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='sqrt',
random_state=100, subsample=0.9))])
cv score: [0.6725 0.5925 0.68666667 0.8 0.81566456]
----------------------------------------
Trial 1618
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
n_estimators=44, random_state=100,
subsample=0.8))])
cv score: [0.71666667 0.67125 0.78666667 0.85583333 0.84572785]
----------------------------------------
Trial 1619
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=26, random_state=100))])
cv score: [0.69791667 0.69375 0.70125 0.84958333 0.88132911]
----------------------------------------
Trial 1620
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=152,
random_state=100))])
cv score: [0.56791667 0.66541667 0.69416667 0.82375 0.81012658]
----------------------------------------
Trial 1621
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.8))])
cv score: [0.64916667 0.64333333 0.70166667 0.80583333 0.84098101]
----------------------------------------
Trial 1622
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70666667 0.74 0.78375 0.855 0.88053797]
----------------------------------------
Trial 1623
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=67, random_state=100))])
cv score: [0.6325 0.6625 0.74416667 0.77916667 0.82990506]
----------------------------------------
Trial 1624
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.95))])
cv score: [0.5625 0.60166667 0.68166667 0.73083333 0.77848101]
----------------------------------------
Trial 1625
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=179, random_state=100))])
cv score: [0.57416667 0.70333333 0.70583333 0.81166667 0.8125 ]
----------------------------------------
Trial 1626
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=97,
random_state=100))])
cv score: [0.62833333 0.5925 0.735 0.7825 0.75830696]
----------------------------------------
Trial 1627
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=63, random_state=100,
subsample=0.6))])
cv score: [0.61416667 0.56416667 0.58583333 0.68916667 0.6835443 ]
----------------------------------------
Trial 1628
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67583333 0.68083333 0.76416667 0.80083333 0.86867089]
----------------------------------------
Trial 1629
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=190, random_state=100,
subsample=0.95))])
cv score: [0.66166667 0.6075 0.69916667 0.79083333 0.83623418]
----------------------------------------
Trial 1630
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=72, random_state=100))])
cv score: [0.75583333 0.68416667 0.7575 0.8375 0.87658228]
----------------------------------------
Trial 1631
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6825 0.6225 0.71416667 0.73333333 0.82199367]
----------------------------------------
Trial 1632
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=35, random_state=100,
subsample=0.6))])
cv score: [0.75416667 0.66083333 0.71083333 0.78166667 0.8125 ]
----------------------------------------
Trial 1633
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=92,
random_state=100))])
cv score: [0.64666667 0.66916667 0.70333333 0.80583333 0.84335443]
----------------------------------------
Trial 1634
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=127,
random_state=100))])
cv score: [0.64458333 0.58 0.74416667 0.78291667 0.77373418]
----------------------------------------
Trial 1635
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=80,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.7375 0.78583333 0.86083333 0.88686709]
----------------------------------------
Trial 1636
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=109,
random_state=100))])
cv score: [0.69833333 0.6025 0.7075 0.77583333 0.8085443 ]
----------------------------------------
Trial 1637
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=169,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63166667 0.62916667 0.78583333 0.74333333 0.79351266]
----------------------------------------
Trial 1638
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=28, random_state=100))])
cv score: [0.65791667 0.65208333 0.7125 0.80791667 0.82515823]
----------------------------------------
Trial 1639
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=164,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59333333 0.53833333 0.7975 0.69416667 0.73575949]
----------------------------------------
Trial 1640
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=37,
random_state=100))])
cv score: [0.68666667 0.57625 0.71083333 0.82166667 0.82753165]
----------------------------------------
Trial 1641
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71 0.68 0.80416667 0.77916667 0.89794304]
----------------------------------------
Trial 1642
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='sqrt',
n_estimators=186, random_state=100,
subsample=0.7))])
cv score: [0.62416667 0.655 0.595 0.67583333 0.68987342]
----------------------------------------
Trial 1643
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.68833333 0.745 0.77166667 0.87579114]
----------------------------------------
Trial 1644
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=145, random_state=100))])
cv score: [0.73666667 0.67791667 0.77875 0.82916667 0.82832278]
----------------------------------------
Trial 1645
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='sqrt',
n_estimators=39, random_state=100,
subsample=0.95))])
cv score: [0.645 0.67916667 0.72166667 0.70083333 0.82753165]
----------------------------------------
Trial 1646
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=53,
random_state=100))])
cv score: [0.695 0.64708333 0.7075 0.7875 0.85363924]
----------------------------------------
Trial 1647
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6425 0.615 0.78416667 0.79 0.83386076]
----------------------------------------
Trial 1648
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61958333 0.69666667 0.67791667 0.81666667 0.78757911]
----------------------------------------
Trial 1649
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=47, random_state=100,
subsample=0.75))])
cv score: [0.77 0.67708333 0.785 0.85083333 0.88528481]
----------------------------------------
Trial 1650
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=89, random_state=100,
subsample=0.8))])
cv score: [0.6575 0.61083333 0.72833333 0.785 0.77848101]
----------------------------------------
Trial 1651
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=153, random_state=100,
subsample=0.9))])
cv score: [0.71666667 0.5975 0.69 0.80666667 0.81882911]
----------------------------------------
Trial 1652
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='log2',
n_estimators=119, random_state=100,
subsample=0.85))])
cv score: [0.60083333 0.52916667 0.4775 0.69 0.6653481 ]
----------------------------------------
Trial 1653
----------------------------------------
Parameters {'gb__n_estimators': 88, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='log2',
n_estimators=88, random_state=100,
subsample=0.9))])
cv score: [0.62333333 0.6025 0.68 0.7075 0.72863924]
----------------------------------------
Trial 1654
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=20, random_state=100))])
cv score: [0.77333333 0.69416667 0.69416667 0.85875 0.88132911]
----------------------------------------
Trial 1655
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=86, random_state=100))])
cv score: [0.74916667 0.67833333 0.77833333 0.82916667 0.85126582]
----------------------------------------
Trial 1656
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='sqrt',
n_estimators=38, random_state=100,
subsample=0.85))])
cv score: [0.67583333 0.61333333 0.70083333 0.77 0.78481013]
----------------------------------------
Trial 1657
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=134,
random_state=100))])
cv score: [0.69416667 0.635 0.70166667 0.805 0.82674051]
----------------------------------------
Trial 1658
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=35,
random_state=100))])
cv score: [0.53333333 0.67125 0.72416667 0.84083333 0.80893987]
----------------------------------------
Trial 1659
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=181, random_state=100))])
cv score: [0.60416667 0.68833333 0.7 0.80166667 0.8346519 ]
----------------------------------------
Trial 1660
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71291667 0.73333333 0.75041667 0.8525 0.87420886]
----------------------------------------
Trial 1661
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58333333 0.61583333 0.73916667 0.67833333 0.7943038 ]
----------------------------------------
Trial 1662
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=188,
random_state=100))])
cv score: [0.6225 0.66666667 0.70583333 0.81666667 0.82753165]
----------------------------------------
Trial 1663
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=117,
random_state=100))])
cv score: [0.5825 0.66666667 0.70416667 0.83416667 0.83386076]
----------------------------------------
Trial 1664
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=111,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6975 0.6425 0.72583333 0.74833333 0.80458861]
----------------------------------------
Trial 1665
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=63, random_state=100))])
cv score: [0.68041667 0.61625 0.73208333 0.80916667 0.79707278]
----------------------------------------
Trial 1666
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=166, random_state=100))])
cv score: [0.60333333 0.6925 0.70583333 0.8125 0.84018987]
----------------------------------------
Trial 1667
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=131, random_state=100,
subsample=0.8))])
cv score: [0.57916667 0.63916667 0.70083333 0.78333333 0.72151899]
----------------------------------------
Trial 1668
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=149, random_state=100))])
cv score: [0.685 0.66333333 0.72 0.81166667 0.85996835]
----------------------------------------
Trial 1669
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=15,
random_state=100))])
cv score: [0.67916667 0.56666667 0.74166667 0.77916667 0.82515823]
----------------------------------------
Trial 1670
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=39,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.73166667 0.78541667 0.84 0.88686709]
----------------------------------------
Trial 1671
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=141, random_state=100,
subsample=0.8))])
cv score: [0.64666667 0.585 0.65833333 0.71333333 0.71044304]
----------------------------------------
Trial 1672
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=137,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77916667 0.73416667 0.78166667 0.84083333 0.90743671]
----------------------------------------
Trial 1673
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73666667 0.74625 0.77583333 0.84333333 0.87262658]
----------------------------------------
Trial 1674
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=185,
random_state=100))])
cv score: [0.58833333 0.66833333 0.70083333 0.8275 0.82832278]
----------------------------------------
Trial 1675
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=57,
random_state=100))])
cv score: [0.65833333 0.58583333 0.6975 0.79333333 0.83939873]
----------------------------------------
Trial 1676
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=42, random_state=100,
subsample=0.9))])
cv score: [0.71583333 0.62416667 0.69166667 0.76166667 0.79272152]
----------------------------------------
Trial 1677
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=174,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6725 0.7125 0.71541667 0.86333333 0.85443038]
----------------------------------------
Trial 1678
----------------------------------------
Parameters {'xgb__n_estimators': 160, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=160,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6025 0.71458333 0.73 0.8225 0.79351266]
----------------------------------------
Trial 1679
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=129, random_state=100,
subsample=0.8))])
cv score: [0.69083333 0.61333333 0.69833333 0.73833333 0.77927215]
----------------------------------------
Trial 1680
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=80,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.6675 0.75083333 0.79416667 0.81962025]
----------------------------------------
Trial 1681
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.66083333 0.78083333 0.8125 0.8931962 ]
----------------------------------------
Trial 1682
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=108, random_state=100,
subsample=0.75))])
cv score: [0.6725 0.61833333 0.70333333 0.805 0.81962025]
----------------------------------------
Trial 1683
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=196, random_state=100,
subsample=0.85))])
cv score: [0.55666667 0.57333333 0.67333333 0.76166667 0.75158228]
----------------------------------------
Trial 1684
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=138, random_state=100,
subsample=0.9))])
cv score: [0.72333333 0.6225 0.82416667 0.84416667 0.78639241]
----------------------------------------
Trial 1685
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=129,
random_state=100))])
cv score: [0.68708333 0.49375 0.81 0.64666667 0.75316456]
----------------------------------------
Trial 1686
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=123,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70833333 0.605 0.7325 0.73083333 0.82594937]
----------------------------------------
Trial 1687
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.62083333 0.7475 0.765 0.83939873]
----------------------------------------
Trial 1688
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70375 0.71666667 0.79083333 0.855 0.87658228]
----------------------------------------
Trial 1689
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=67, random_state=100,
subsample=0.8))])
cv score: [0.515 0.5575 0.6275 0.59916667 0.75553797]
----------------------------------------
Trial 1690
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=147,
random_state=100))])
cv score: [0.69583333 0.6275 0.68833333 0.79666667 0.82753165]
----------------------------------------
Trial 1691
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=94,
random_state=100))])
cv score: [0.70166667 0.61833333 0.69166667 0.77833333 0.80775316]
----------------------------------------
Trial 1692
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76916667 0.66791667 0.77125 0.83083333 0.87816456]
----------------------------------------
Trial 1693
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73333333 0.63083333 0.79416667 0.73833333 0.83544304]
----------------------------------------
Trial 1694
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='log2',
n_estimators=97, random_state=100,
subsample=0.7))])
cv score: [0.57 0.59833333 0.66416667 0.65 0.65901899]
----------------------------------------
Trial 1695
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=75, random_state=100,
subsample=0.85))])
cv score: [0.63083333 0.5575 0.7175 0.8 0.69303797]
----------------------------------------
Trial 1696
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58416667 0.62 0.78083333 0.7225 0.82753165]
----------------------------------------
Trial 1697
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='log2',
n_estimators=28, random_state=100,
subsample=0.7))])
cv score: [0.54 0.63958333 0.62458333 0.8075 0.79351266]
----------------------------------------
Trial 1698
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=110, random_state=100,
subsample=0.95))])
cv score: [0.63166667 0.55833333 0.6825 0.81666667 0.79667722]
----------------------------------------
Trial 1699
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=142, random_state=100,
subsample=0.9))])
cv score: [0.6925 0.59333333 0.705 0.81416667 0.78718354]
----------------------------------------
Trial 1700
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=54, random_state=100))])
cv score: [0.75416667 0.6975 0.7475 0.8525 0.88370253]
----------------------------------------
Trial 1701
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66583333 0.60333333 0.73 0.74166667 0.80933544]
----------------------------------------
Trial 1702
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67416667 0.73333333 0.73833333 0.85958333 0.84691456]
----------------------------------------
Trial 1703
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=118, random_state=100))])
cv score: [0.61333333 0.7 0.70333333 0.79666667 0.82911392]
----------------------------------------
Trial 1704
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=98, random_state=100,
subsample=0.6))])
cv score: [0.73083333 0.67416667 0.7925 0.8275 0.8346519 ]
----------------------------------------
Trial 1705
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=17, random_state=100))])
cv score: [0.725 0.64583333 0.76958333 0.85208333 0.84098101]
----------------------------------------
Trial 1706
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6825 0.71083333 0.78083333 0.82833333 0.90268987]
----------------------------------------
Trial 1707
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=160, random_state=100))])
cv score: [0.72375 0.67625 0.77666667 0.83625 0.82911392]
----------------------------------------
Trial 1708
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.755 0.71083333 0.78333333 0.83666667 0.87974684]
----------------------------------------
Trial 1709
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=122, random_state=100))])
cv score: [0.6575 0.625 0.74166667 0.8125 0.83702532]
----------------------------------------
Trial 1710
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='sqrt',
n_estimators=73, random_state=100,
subsample=0.75))])
cv score: [0.66166667 0.62833333 0.68166667 0.79666667 0.7943038 ]
----------------------------------------
Trial 1711
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=123, random_state=100,
subsample=0.8))])
cv score: [0.7075 0.61 0.68833333 0.80666667 0.81170886]
----------------------------------------
Trial 1712
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='log2',
n_estimators=191, random_state=100,
subsample=0.75))])
cv score: [0.63166667 0.56166667 0.68916667 0.72833333 0.75949367]
----------------------------------------
Trial 1713
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=163,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75916667 0.71916667 0.76083333 0.85416667 0.875 ]
----------------------------------------
Trial 1714
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74583333 0.7225 0.79666667 0.80333333 0.86155063]
----------------------------------------
Trial 1715
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=73, random_state=100,
subsample=0.95))])
cv score: [0.675 0.5875 0.71333333 0.795 0.81012658]
----------------------------------------
Trial 1716
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=58,
random_state=100))])
cv score: [0.66833333 0.58875 0.69 0.79833333 0.81724684]
----------------------------------------
Trial 1717
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78291667 0.7225 0.77541667 0.86208333 0.85403481]
----------------------------------------
Trial 1718
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=70,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66041667 0.71458333 0.74791667 0.85625 0.87935127]
----------------------------------------
Trial 1719
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=172,
random_state=100))])
cv score: [0.65333333 0.67083333 0.69916667 0.8275 0.83939873]
----------------------------------------
Trial 1720
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=145, random_state=100))])
cv score: [0.725 0.68375 0.78333333 0.83583333 0.83781646]
----------------------------------------
Trial 1721
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='sqrt',
n_estimators=132, random_state=100,
subsample=0.65))])
cv score: [0.69416667 0.65166667 0.72916667 0.81916667 0.81329114]
----------------------------------------
Trial 1722
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6825 0.6375 0.735 0.76 0.84889241]
----------------------------------------
Trial 1723
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
n_estimators=96, random_state=100,
subsample=0.75))])
cv score: [0.66833333 0.67 0.60083333 0.6925 0.69303797]
----------------------------------------
Trial 1724
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=143,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.66291667 0.78876582]
----------------------------------------
Trial 1725
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=152, random_state=100,
subsample=0.8))])
cv score: [0.53416667 0.62083333 0.7175 0.8025 0.75 ]
----------------------------------------
Trial 1726
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=199,
random_state=100))])
cv score: [0.6525 0.6725 0.7 0.8225 0.83939873]
----------------------------------------
Trial 1727
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=118, random_state=100,
subsample=0.75))])
cv score: [0.645 0.66416667 0.71916667 0.8175 0.86075949]
----------------------------------------
Trial 1728
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=114,
random_state=100))])
cv score: [0.56708333 0.65708333 0.69166667 0.83208333 0.80023734]
----------------------------------------
Trial 1729
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=164, random_state=100,
subsample=0.85))])
cv score: [0.73083333 0.655 0.80666667 0.82833333 0.82278481]
----------------------------------------
Trial 1730
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79 0.72208333 0.78083333 0.84583333 0.87658228]
----------------------------------------
Trial 1731
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=154, random_state=100))])
cv score: [0.57583333 0.70666667 0.6925 0.81166667 0.80300633]
----------------------------------------
Trial 1732
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=181, random_state=100))])
cv score: [0.71333333 0.62833333 0.725 0.79916667 0.82515823]
----------------------------------------
Trial 1733
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=73, random_state=100))])
cv score: [0.75083333 0.6725 0.7775 0.82833333 0.85205696]
----------------------------------------
Trial 1734
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=158,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76083333 0.73666667 0.78166667 0.87333333 0.86550633]
----------------------------------------
Trial 1735
----------------------------------------
Parameters {'gb__n_estimators': 177, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
n_estimators=177, random_state=100,
subsample=0.8))])
cv score: [0.66666667 0.62166667 0.67416667 0.73583333 0.75712025]
----------------------------------------
Trial 1736
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=165,
random_state=100))])
cv score: [0.6925 0.50125 0.72375 0.64875 0.63291139]
----------------------------------------
Trial 1737
----------------------------------------
Parameters {'xgb__n_estimators': 56, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=56,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59 0.69666667 0.71833333 0.62416667 0.76107595]
----------------------------------------
Trial 1738
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7525 0.75333333 0.78541667 0.86416667 0.85522152]
----------------------------------------
Trial 1739
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=82,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.54083333 0.63666667 0.7125 0.73666667 0.76740506]
----------------------------------------
Trial 1740
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76041667 0.74541667 0.76791667 0.85333333 0.86392405]
----------------------------------------
Trial 1741
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68 0.66416667 0.7675 0.8075 0.79272152]
----------------------------------------
Trial 1742
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=179, random_state=100))])
cv score: [0.74166667 0.67583333 0.77833333 0.83666667 0.84731013]
----------------------------------------
Trial 1743
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.85))])
cv score: [0.62916667 0.6625 0.715 0.82083333 0.81882911]
----------------------------------------
Trial 1744
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
n_estimators=14, random_state=100,
subsample=0.75))])
cv score: [0.65 0.68416667 0.77291667 0.79 0.80221519]
----------------------------------------
Trial 1745
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=49,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.715 0.81 0.84916667 0.8931962 ]
----------------------------------------
Trial 1746
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=92,
random_state=100))])
cv score: [0.75166667 0.58791667 0.80833333 0.79041667 0.80458861]
----------------------------------------
Trial 1747
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76666667 0.74208333 0.77 0.835 0.89240506]
----------------------------------------
Trial 1748
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=175, random_state=100))])
cv score: [0.66333333 0.66 0.70333333 0.82416667 0.85759494]
----------------------------------------
Trial 1749
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=76, random_state=100))])
cv score: [0.57333333 0.72625 0.68916667 0.79083333 0.80458861]
----------------------------------------
Trial 1750
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='sqrt',
n_estimators=27, random_state=100,
subsample=0.9))])
cv score: [0.655 0.59416667 0.695 0.71583333 0.75474684]
----------------------------------------
Trial 1751
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77833333 0.74625 0.8025 0.855 0.85917722]
----------------------------------------
Trial 1752
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=110,
random_state=100))])
cv score: [0.68916667 0.63 0.69166667 0.79583333 0.80933544]
----------------------------------------
Trial 1753
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=18,
random_state=100))])
cv score: [0.7 0.53375 0.8225 0.69083333 0.71083861]
----------------------------------------
Trial 1754
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74166667 0.70666667 0.79416667 0.81333333 0.86867089]
----------------------------------------
Trial 1755
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=163,
random_state=100))])
cv score: [0.6825 0.59333333 0.7025 0.795 0.81170886]
----------------------------------------
Trial 1756
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=118, random_state=100,
subsample=0.95))])
cv score: [0.6825 0.62833333 0.67416667 0.76416667 0.78243671]
----------------------------------------
Trial 1757
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=197, random_state=100,
subsample=0.9))])
cv score: [0.68916667 0.60416667 0.7075 0.78 0.79667722]
----------------------------------------
Trial 1758
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=37,
random_state=100))])
cv score: [0.66416667 0.50541667 0.79833333 0.66125 0.78876582]
----------------------------------------
Trial 1759
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=132,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 1760
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=179,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.785 0.74583333 0.80166667 0.85166667 0.86392405]
----------------------------------------
Trial 1761
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=164,
random_state=100))])
cv score: [0.7525 0.60833333 0.78166667 0.8375 0.82199367]
----------------------------------------
Trial 1762
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72083333 0.6125 0.77083333 0.7575 0.78876582]
----------------------------------------
Trial 1763
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=140,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.6675 0.78416667 0.80083333 0.90268987]
----------------------------------------
Trial 1764
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=107, random_state=100))])
cv score: [0.7325 0.68208333 0.78916667 0.8325 0.84889241]
----------------------------------------
Trial 1765
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
n_estimators=199, random_state=100,
subsample=0.75))])
cv score: [0.61666667 0.6375 0.75083333 0.73833333 0.76740506]
----------------------------------------
Trial 1766
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=45,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 1767
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=15, random_state=100,
subsample=0.85))])
cv score: [0.55416667 0.55666667 0.70833333 0.74833333 0.83227848]
----------------------------------------
Trial 1768
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=110, random_state=100,
subsample=0.95))])
cv score: [0.7275 0.63916667 0.81416667 0.83 0.77768987]
----------------------------------------
Trial 1769
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=179, random_state=100,
subsample=0.9))])
cv score: [0.69916667 0.61 0.71 0.7825 0.7903481 ]
----------------------------------------
Trial 1770
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5875 0.65666667 0.76 0.72666667 0.75870253]
----------------------------------------
Trial 1771
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=65, random_state=100,
subsample=0.9))])
cv score: [0.58833333 0.57666667 0.62666667 0.67833333 0.67721519]
----------------------------------------
Trial 1772
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=85, random_state=100,
subsample=0.85))])
cv score: [0.6625 0.60583333 0.655 0.79916667 0.80458861]
----------------------------------------
Trial 1773
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=110,
random_state=100))])
cv score: [0.69333333 0.4875 0.72166667 0.66958333 0.63647152]
----------------------------------------
Trial 1774
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70958333 0.74208333 0.74541667 0.86833333 0.84889241]
----------------------------------------
Trial 1775
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=25,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68083333 0.73333333 0.75791667 0.84041667 0.87658228]
----------------------------------------
Trial 1776
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74166667 0.68833333 0.79083333 0.81583333 0.8789557 ]
----------------------------------------
Trial 1777
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='log2',
n_estimators=82, random_state=100,
subsample=0.75))])
cv score: [0.555 0.58916667 0.66 0.6625 0.72072785]
----------------------------------------
Trial 1778
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=167,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 1779
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78416667 0.71541667 0.77208333 0.83666667 0.86313291]
----------------------------------------
Trial 1780
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=44, random_state=100))])
cv score: [0.73625 0.69541667 0.69958333 0.84916667 0.87183544]
----------------------------------------
Trial 1781
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=111,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76833333 0.7475 0.78125 0.86333333 0.86867089]
----------------------------------------
Trial 1782
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='log2',
n_estimators=37, random_state=100,
subsample=0.65))])
cv score: [0.5775 0.66 0.67666667 0.82916667 0.79905063]
----------------------------------------
Trial 1783
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7375 0.73541667 0.7925 0.84916667 0.89161392]
----------------------------------------
Trial 1784
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=65, random_state=100))])
cv score: [0.605 0.675 0.71333333 0.78916667 0.83544304]
----------------------------------------
Trial 1785
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=69,
random_state=100))])
cv score: [0.69 0.61583333 0.67083333 0.7775 0.81882911]
----------------------------------------
Trial 1786
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78 0.73833333 0.77666667 0.86416667 0.88370253]
----------------------------------------
Trial 1787
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=104,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70333333 0.69416667 0.77166667 0.82083333 0.91218354]
----------------------------------------
Trial 1788
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=194,
random_state=100))])
cv score: [0.7525 0.60833333 0.78166667 0.83833333 0.82199367]
----------------------------------------
Trial 1789
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=77,
random_state=100))])
cv score: [0.6 0.68041667 0.69333333 0.80666667 0.82674051]
----------------------------------------
Trial 1790
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=17, random_state=100))])
cv score: [0.70791667 0.66375 0.70458333 0.87625 0.86708861]
----------------------------------------
Trial 1791
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=149, random_state=100))])
cv score: [0.67833333 0.6225 0.73416667 0.80166667 0.83544304]
----------------------------------------
Trial 1792
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=105, random_state=100,
subsample=0.9))])
cv score: [0.6175 0.53083333 0.6275 0.71916667 0.82278481]
----------------------------------------
Trial 1793
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.8))])
cv score: [0.68083333 0.60666667 0.6925 0.78083333 0.82674051]
----------------------------------------
Trial 1794
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=60,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.73333333 0.80583333 0.82833333 0.875 ]
----------------------------------------
Trial 1795
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=144,
random_state=100))])
cv score: [0.64833333 0.61666667 0.69083333 0.76833333 0.73575949]
----------------------------------------
Trial 1796
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=132,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 1797
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='log2',
n_estimators=114,
random_state=100))])
cv score: [0.55416667 0.54083333 0.6525 0.71416667 0.73892405]
----------------------------------------
Trial 1798
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=42,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59083333 0.62833333 0.75583333 0.72666667 0.68433544]
----------------------------------------
Trial 1799
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=102, random_state=100,
subsample=0.85))])
cv score: [0.72 0.64083333 0.805 0.8175 0.80775316]
----------------------------------------
Trial 1800
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, n_estimators=123,
random_state=100,
subsample=0.95))])
cv score: [0.72166667 0.67083333 0.76833333 0.80583333 0.76107595]
----------------------------------------
Trial 1801
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7525 0.7275 0.7975 0.84666667 0.87816456]
----------------------------------------
Trial 1802
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63791667 0.74458333 0.74916667 0.8625 0.84889241]
----------------------------------------
Trial 1803
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=155, random_state=100))])
cv score: [0.69916667 0.65 0.7175 0.79916667 0.84098101]
----------------------------------------
Trial 1804
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=10,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72833333 0.72458333 0.78791667 0.82666667 0.90901899]
----------------------------------------
Trial 1805
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=56, random_state=100,
subsample=0.7))])
cv score: [0.69583333 0.5675 0.75916667 0.79666667 0.68908228]
----------------------------------------
Trial 1806
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=117,
random_state=100))])
cv score: [0.56708333 0.65875 0.69083333 0.83041667 0.8034019 ]
----------------------------------------
Trial 1807
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67208333 0.71875 0.76583333 0.8275 0.87064873]
----------------------------------------
Trial 1808
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7725 0.71916667 0.785 0.84 0.89477848]
----------------------------------------
Trial 1809
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=197,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57083333 0.71291667 0.70416667 0.82416667 0.80221519]
----------------------------------------
Trial 1810
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=57,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76166667 0.74 0.805 0.85083333 0.87816456]
----------------------------------------
Trial 1811
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=97, random_state=100,
subsample=0.85))])
cv score: [0.6525 0.5475 0.65583333 0.74166667 0.69936709]
----------------------------------------
Trial 1812
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=198, random_state=100,
subsample=0.6))])
cv score: [0.67333333 0.62166667 0.70916667 0.70083333 0.70886076]
----------------------------------------
Trial 1813
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=36,
random_state=100))])
cv score: [0.6375 0.65333333 0.77416667 0.82083333 0.87025316]
----------------------------------------
Trial 1814
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=17,
random_state=100, subsample=0.9))])
cv score: [0.60625 0.57708333 0.79375 0.77166667 0.7721519 ]
----------------------------------------
Trial 1815
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=62,
random_state=100))])
cv score: [0.63916667 0.60666667 0.70583333 0.78833333 0.79113924]
----------------------------------------
Trial 1816
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=192,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.7175 0.76291667 0.85083333 0.85759494]
----------------------------------------
Trial 1817
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=183,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72916667 0.71916667 0.76333333 0.86083333 0.86550633]
----------------------------------------
Trial 1818
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.7225 0.78 0.84416667 0.9153481 ]
----------------------------------------
Trial 1819
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=90,
random_state=100))])
cv score: [0.64333333 0.66916667 0.70416667 0.8075 0.84177215]
----------------------------------------
Trial 1820
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60916667 0.61333333 0.7675 0.715 0.82674051]
----------------------------------------
Trial 1821
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=147, random_state=100))])
cv score: [0.73291667 0.67583333 0.77958333 0.83125 0.82753165]
----------------------------------------
Trial 1822
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=146, random_state=100))])
cv score: [0.77916667 0.68 0.74833333 0.85083333 0.90348101]
----------------------------------------
Trial 1823
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=172,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7775 0.73916667 0.77083333 0.83875 0.87816456]
----------------------------------------
Trial 1824
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.705 0.69583333 0.78416667 0.79333333 0.89003165]
----------------------------------------
Trial 1825
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=121,
random_state=100))])
cv score: [0.58583333 0.66833333 0.70666667 0.83333333 0.83148734]
----------------------------------------
Trial 1826
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.75))])
cv score: [0.6025 0.635 0.555 0.68083333 0.77689873]
----------------------------------------
Trial 1827
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6925 0.72583333 0.75458333 0.8425 0.85126582]
----------------------------------------
Trial 1828
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71583333 0.72333333 0.77041667 0.85916667 0.85601266]
----------------------------------------
Trial 1829
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=175,
random_state=100))])
cv score: [0.76458333 0.49375 0.78708333 0.70208333 0.79232595]
----------------------------------------
Trial 1830
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=130, random_state=100))])
cv score: [0.69083333 0.62916667 0.72 0.80333333 0.85047468]
----------------------------------------
Trial 1831
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=140, random_state=100))])
cv score: [0.69333333 0.6225 0.72 0.79916667 0.85363924]
----------------------------------------
Trial 1832
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=62,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65166667 0.665 0.72 0.73166667 0.73496835]
----------------------------------------
Trial 1833
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.65416667 0.77833333 0.77333333 0.83544304]
----------------------------------------
Trial 1834
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=117,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.765 0.71416667 0.77083333 0.83291667 0.90585443]
----------------------------------------
Trial 1835
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='sqrt',
n_estimators=99, random_state=100,
subsample=0.85))])
cv score: [0.58916667 0.57666667 0.65666667 0.77833333 0.77531646]
----------------------------------------
Trial 1836
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=120, random_state=100,
subsample=0.75))])
cv score: [0.66666667 0.66583333 0.76416667 0.8275 0.83939873]
----------------------------------------
Trial 1837
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=173, random_state=100,
subsample=0.65))])
cv score: [0.73333333 0.73416667 0.75166667 0.84916667 0.87658228]
----------------------------------------
Trial 1838
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=71, random_state=100,
subsample=0.85))])
cv score: [0.64416667 0.605 0.69916667 0.80166667 0.81091772]
----------------------------------------
Trial 1839
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=27, random_state=100))])
cv score: [0.64958333 0.71458333 0.70458333 0.81833333 0.90545886]
----------------------------------------
Trial 1840
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=101, random_state=100,
subsample=0.95))])
cv score: [0.74333333 0.64416667 0.7275 0.74333333 0.75079114]
----------------------------------------
Trial 1841
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=176, random_state=100,
subsample=0.8))])
cv score: [0.68 0.58666667 0.72416667 0.80333333 0.80221519]
----------------------------------------
Trial 1842
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=106, random_state=100))])
cv score: [0.65666667 0.66 0.68666667 0.82666667 0.8568038 ]
----------------------------------------
Trial 1843
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69666667 0.655 0.74416667 0.79333333 0.85205696]
----------------------------------------
Trial 1844
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=18, random_state=100))])
cv score: [0.57333333 0.57166667 0.71 0.7425 0.82990506]
----------------------------------------
Trial 1845
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=29, random_state=100,
subsample=0.95))])
cv score: [0.5175 0.59208333 0.67875 0.785 0.75751582]
----------------------------------------
Trial 1846
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=25, random_state=100,
subsample=0.75))])
cv score: [0.6875 0.61583333 0.57583333 0.46833333 0.50158228]
----------------------------------------
Trial 1847
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, n_estimators=77,
random_state=100,
subsample=0.75))])
cv score: [0.71333333 0.64 0.7625 0.80833333 0.79272152]
----------------------------------------
Trial 1848
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=19,
random_state=100))])
cv score: [0.6625 0.605 0.72875 0.80208333 0.77689873]
----------------------------------------
Trial 1849
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=24,
random_state=100))])
cv score: [0.62875 0.45375 0.79375 0.77625 0.74208861]
----------------------------------------
Trial 1850
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=122,
random_state=100))])
cv score: [0.63666667 0.57583333 0.74333333 0.78416667 0.77927215]
----------------------------------------
Trial 1851
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='sqrt',
n_estimators=96, random_state=100,
subsample=0.95))])
cv score: [0.66083333 0.5925 0.71666667 0.75333333 0.79351266]
----------------------------------------
Trial 1852
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=186,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6775 0.64333333 0.78666667 0.77833333 0.80933544]
----------------------------------------
Trial 1853
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=84,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 1854
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6775 0.63083333 0.78166667 0.75916667 0.83544304]
----------------------------------------
Trial 1855
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.73708333 0.76208333 0.86833333 0.85522152]
----------------------------------------
Trial 1856
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=122, random_state=100,
subsample=0.7))])
cv score: [0.62 0.6025 0.64583333 0.7025 0.68512658]
----------------------------------------
Trial 1857
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=19,
random_state=100))])
cv score: [0.68916667 0.48916667 0.78208333 0.67291667 0.63884494]
----------------------------------------
Trial 1858
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=182, random_state=100,
subsample=0.8))])
cv score: [0.615 0.565 0.6425 0.70083333 0.63132911]
----------------------------------------
Trial 1859
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=113,
random_state=100))])
cv score: [0.6675 0.60666667 0.71833333 0.7975 0.73417722]
----------------------------------------
Trial 1860
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=73,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6475 0.64833333 0.73416667 0.76083333 0.76898734]
----------------------------------------
Trial 1861
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=128,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.72333333 0.76791667 0.8575 0.85363924]
----------------------------------------
Trial 1862
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67416667 0.64583333 0.765 0.7 0.79272152]
----------------------------------------
Trial 1863
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=35, random_state=100))])
cv score: [0.67083333 0.62333333 0.70833333 0.78 0.84177215]
----------------------------------------
Trial 1864
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=60,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63 0.67166667 0.72916667 0.715 0.7721519 ]
----------------------------------------
Trial 1865
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66666667 0.60916667 0.72333333 0.6925 0.66139241]
----------------------------------------
Trial 1866
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=126,
random_state=100))])
cv score: [0.69291667 0.48916667 0.77916667 0.67041667 0.63607595]
----------------------------------------
Trial 1867
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=144, random_state=100,
subsample=0.75))])
cv score: [0.64166667 0.6475 0.7125 0.815 0.82436709]
----------------------------------------
Trial 1868
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72 0.68416667 0.80166667 0.8175 0.89636076]
----------------------------------------
Trial 1869
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78375 0.72583333 0.765 0.85166667 0.86629747]
----------------------------------------
Trial 1870
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.65))])
cv score: [0.64583333 0.5875 0.72916667 0.80083333 0.82753165]
----------------------------------------
Trial 1871
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72666667 0.73583333 0.77666667 0.86083333 0.86155063]
----------------------------------------
Trial 1872
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=110,
random_state=100))])
cv score: [0.68166667 0.61083333 0.72083333 0.80916667 0.82278481]
----------------------------------------
Trial 1873
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.75))])
cv score: [0.67833333 0.5925 0.71 0.79583333 0.78322785]
----------------------------------------
Trial 1874
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=106,
random_state=100))])
cv score: [0.6475 0.66333333 0.69583333 0.8075 0.84335443]
----------------------------------------
Trial 1875
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=135,
random_state=100))])
cv score: [0.6925 0.48916667 0.77791667 0.67 0.63607595]
----------------------------------------
Trial 1876
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='log2',
n_estimators=160, random_state=100,
subsample=0.65))])
cv score: [0.38666667 0.71083333 0.46083333 0.60916667 0.60007911]
----------------------------------------
Trial 1877
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.75041667 0.78416667 0.85708333 0.85205696]
----------------------------------------
Trial 1878
----------------------------------------
Parameters {'xgb__n_estimators': 95, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=95,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.705 0.66666667 0.735 0.79166667 0.86787975]
----------------------------------------
Trial 1879
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=171, random_state=100))])
cv score: [0.66583333 0.65666667 0.70083333 0.81916667 0.85838608]
----------------------------------------
Trial 1880
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=30,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68333333 0.74541667 0.75583333 0.8375 0.87183544]
----------------------------------------
Trial 1881
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=181, random_state=100))])
cv score: [0.69666667 0.64166667 0.70916667 0.80166667 0.83939873]
----------------------------------------
Trial 1882
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=15,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 1883
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=137, random_state=100))])
cv score: [0.725 0.68541667 0.78583333 0.83416667 0.83623418]
----------------------------------------
Trial 1884
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=117, random_state=100))])
cv score: [0.685 0.64166667 0.73083333 0.81583333 0.83148734]
----------------------------------------
Trial 1885
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=57,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7675 0.70666667 0.78166667 0.82166667 0.83306962]
----------------------------------------
Trial 1886
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=39,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.72916667 0.73125 0.83875 0.85759494]
----------------------------------------
Trial 1887
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65416667 0.66166667 0.76 0.77583333 0.82990506]
----------------------------------------
Trial 1888
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=69, random_state=100,
subsample=0.95))])
cv score: [0.57333333 0.6025 0.59916667 0.7125 0.69936709]
----------------------------------------
Trial 1889
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='sqrt',
n_estimators=94, random_state=100,
subsample=0.7))])
cv score: [0.67 0.65583333 0.65166667 0.76083333 0.78876582]
----------------------------------------
Trial 1890
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=15,
random_state=100))])
cv score: [0.67958333 0.51375 0.83208333 0.665 0.66890823]
----------------------------------------
Trial 1891
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.8))])
cv score: [0.5975 0.54166667 0.61 0.71583333 0.70094937]
----------------------------------------
Trial 1892
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='sqrt',
n_estimators=115, random_state=100,
subsample=0.7))])
cv score: [0.61833333 0.68666667 0.68916667 0.8375 0.83544304]
----------------------------------------
Trial 1893
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=66, random_state=100))])
cv score: [0.7375 0.66333333 0.77958333 0.82666667 0.8397943 ]
----------------------------------------
Trial 1894
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75833333 0.715 0.78833333 0.84 0.92088608]
----------------------------------------
Trial 1895
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=151,
random_state=100))])
cv score: [0.6925 0.4875 0.71875 0.66916667 0.63607595]
----------------------------------------
Trial 1896
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=51,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7525 0.715 0.78666667 0.83416667 0.88924051]
----------------------------------------
Trial 1897
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='sqrt',
n_estimators=121, random_state=100,
subsample=0.6))])
cv score: [0.6525 0.60833333 0.675 0.80666667 0.78481013]
----------------------------------------
Trial 1898
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=104, random_state=100,
subsample=0.8))])
cv score: [0.55916667 0.67833333 0.67833333 0.71083333 0.63924051]
----------------------------------------
Trial 1899
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79333333 0.7 0.76583333 0.85833333 0.89636076]
----------------------------------------
Trial 1900
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76 0.7325 0.77666667 0.85833333 0.86629747]
----------------------------------------
Trial 1901
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=152,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67583333 0.70916667 0.74458333 0.87833333 0.84810127]
----------------------------------------
Trial 1902
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=158,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5925 0.61 0.73666667 0.6675 0.78164557]
----------------------------------------
Trial 1903
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=164,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73583333 0.69083333 0.80916667 0.83916667 0.87737342]
----------------------------------------
Trial 1904
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=85,
random_state=100))])
cv score: [0.69458333 0.51791667 0.79916667 0.66916667 0.62974684]
----------------------------------------
Trial 1905
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69166667 0.58166667 0.75083333 0.77666667 0.78955696]
----------------------------------------
Trial 1906
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=132, random_state=100))])
cv score: [0.6825 0.65333333 0.72666667 0.8075 0.85522152]
----------------------------------------
Trial 1907
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
n_estimators=125, random_state=100,
subsample=0.65))])
cv score: [0.58 0.58916667 0.64916667 0.71916667 0.61867089]
----------------------------------------
Trial 1908
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=49,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67 0.70583333 0.73458333 0.84333333 0.8619462 ]
----------------------------------------
Trial 1909
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62916667 0.655 0.77583333 0.785 0.82041139]
----------------------------------------
Trial 1910
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=117, random_state=100,
subsample=0.7))])
cv score: [0.755 0.67083333 0.74583333 0.83583333 0.88765823]
----------------------------------------
Trial 1911
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6975 0.66333333 0.77833333 0.78083333 0.86708861]
----------------------------------------
Trial 1912
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68583333 0.62916667 0.77666667 0.77916667 0.83148734]
----------------------------------------
Trial 1913
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=149, random_state=100,
subsample=0.7))])
cv score: [0.68083333 0.64 0.71 0.8025 0.81882911]
----------------------------------------
Trial 1914
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=40, random_state=100))])
cv score: [0.66166667 0.61875 0.73833333 0.83625 0.78876582]
----------------------------------------
Trial 1915
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=161, random_state=100))])
cv score: [0.77833333 0.6975 0.775 0.85333333 0.8710443 ]
----------------------------------------
Trial 1916
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=118,
random_state=100))])
cv score: [0.69333333 0.4875 0.72041667 0.66875 0.63647152]
----------------------------------------
Trial 1917
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=83, random_state=100,
subsample=0.75))])
cv score: [0.67083333 0.60083333 0.73583333 0.82166667 0.76740506]
----------------------------------------
Trial 1918
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=90, random_state=100,
subsample=0.9))])
cv score: [0.70666667 0.60916667 0.72833333 0.7875 0.83860759]
----------------------------------------
Trial 1919
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=192,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.68666667 0.79833333 0.81416667 0.89082278]
----------------------------------------
Trial 1920
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=46, random_state=100,
subsample=0.7))])
cv score: [0.6575 0.62 0.6475 0.82416667 0.83939873]
----------------------------------------
Trial 1921
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=30, random_state=100))])
cv score: [0.61583333 0.61666667 0.74958333 0.83458333 0.81526899]
----------------------------------------
Trial 1922
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=68,
random_state=100))])
cv score: [0.55958333 0.68208333 0.71208333 0.82625 0.79865506]
----------------------------------------
Trial 1923
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=179,
random_state=100))])
cv score: [0.68708333 0.49375 0.80875 0.64625 0.75276899]
----------------------------------------
Trial 1924
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=122, random_state=100,
subsample=0.9))])
cv score: [0.66833333 0.58416667 0.7125 0.7925 0.80063291]
----------------------------------------
Trial 1925
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=150, random_state=100,
subsample=0.8))])
cv score: [0.6875 0.67333333 0.77583333 0.82 0.82357595]
----------------------------------------
Trial 1926
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.6175 0.7775 0.7075 0.80617089]
----------------------------------------
Trial 1927
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=86, random_state=100))])
cv score: [0.7125 0.6375 0.74666667 0.7825 0.82674051]
----------------------------------------
Trial 1928
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=71, random_state=100,
subsample=0.95))])
cv score: [0.64 0.51666667 0.69583333 0.76833333 0.6914557 ]
----------------------------------------
Trial 1929
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75041667 0.72041667 0.76208333 0.85458333 0.89636076]
----------------------------------------
Trial 1930
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72916667 0.65458333 0.70833333 0.82 0.78560127]
----------------------------------------
Trial 1931
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=143, random_state=100,
subsample=0.8))])
cv score: [0.67083333 0.6325 0.70583333 0.79166667 0.8346519 ]
----------------------------------------
Trial 1932
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=47,
random_state=100))])
cv score: [0.63583333 0.66791667 0.685 0.80791667 0.84335443]
----------------------------------------
Trial 1933
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68583333 0.67 0.79833333 0.77333333 0.83781646]
----------------------------------------
Trial 1934
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=90, random_state=100))])
cv score: [0.69833333 0.61958333 0.73 0.82666667 0.83030063]
----------------------------------------
Trial 1935
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=80,
random_state=100))])
cv score: [0.6825 0.59 0.67416667 0.8075 0.83623418]
----------------------------------------
Trial 1936
----------------------------------------
Parameters {'rf__n_estimators': 25, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=25, random_state=100))])
cv score: [0.61541667 0.675 0.63125 0.81083333 0.79707278]
----------------------------------------
Trial 1937
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.8))])
cv score: [0.69083333 0.6075 0.69916667 0.7875 0.7721519 ]
----------------------------------------
Trial 1938
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=62, random_state=100,
subsample=0.7))])
cv score: [0.67666667 0.6025 0.71666667 0.7925 0.82041139]
----------------------------------------
Trial 1939
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.635 0.66916667 0.74166667 0.7425 0.86708861]
----------------------------------------
Trial 1940
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='sqrt',
n_estimators=34, random_state=100,
subsample=0.8))])
cv score: [0.63 0.61416667 0.70166667 0.82083333 0.85996835]
----------------------------------------
Trial 1941
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68416667 0.72791667 0.76333333 0.86666667 0.84889241]
----------------------------------------
Trial 1942
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=35, random_state=100))])
cv score: [0.66875 0.61375 0.71166667 0.76583333 0.82950949]
----------------------------------------
Trial 1943
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=96, random_state=100))])
cv score: [0.7825 0.68 0.75166667 0.85 0.87262658]
----------------------------------------
Trial 1944
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
n_estimators=170, random_state=100,
subsample=0.6))])
cv score: [0.60416667 0.49833333 0.67916667 0.66916667 0.65110759]
----------------------------------------
Trial 1945
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=102, random_state=100))])
cv score: [0.75666667 0.69416667 0.77916667 0.83416667 0.8568038 ]
----------------------------------------
Trial 1946
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76583333 0.63916667 0.79583333 0.825 0.84018987]
----------------------------------------
Trial 1947
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=113, random_state=100,
subsample=0.7))])
cv score: [0.7025 0.61833333 0.67416667 0.78666667 0.80063291]
----------------------------------------
Trial 1948
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
n_estimators=117, random_state=100,
subsample=0.85))])
cv score: [0.745 0.64958333 0.79791667 0.83166667 0.83386076]
----------------------------------------
Trial 1949
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=69,
random_state=100))])
cv score: [0.64583333 0.6575 0.6925 0.81 0.84889241]
----------------------------------------
Trial 1950
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=149, random_state=100,
subsample=0.75))])
cv score: [0.58916667 0.57166667 0.7175 0.74666667 0.75553797]
----------------------------------------
Trial 1951
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=187,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66666667 0.615 0.75166667 0.65875 0.73813291]
----------------------------------------
Trial 1952
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='log2',
n_estimators=137,
random_state=100))])
cv score: [0.70083333 0.57583333 0.72416667 0.80833333 0.7943038 ]
----------------------------------------
Trial 1953
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=135,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76416667 0.7225 0.77416667 0.86166667 0.88607595]
----------------------------------------
Trial 1954
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=91, random_state=100))])
cv score: [0.72583333 0.66041667 0.78166667 0.83541667 0.82990506]
----------------------------------------
Trial 1955
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66083333 0.72208333 0.75458333 0.84583333 0.86985759]
----------------------------------------
Trial 1956
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=51,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78416667 0.73791667 0.78708333 0.86083333 0.87579114]
----------------------------------------
Trial 1957
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61 0.67083333 0.73 0.7925 0.76661392]
----------------------------------------
Trial 1958
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.68583333 0.78666667 0.81916667 0.92246835]
----------------------------------------
Trial 1959
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=43, random_state=100))])
cv score: [0.69083333 0.6325 0.7375 0.8175 0.85443038]
----------------------------------------
Trial 1960
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=51,
random_state=100))])
cv score: [0.70166667 0.64416667 0.6675 0.79166667 0.81566456]
----------------------------------------
Trial 1961
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=166, random_state=100))])
cv score: [0.70166667 0.61458333 0.73916667 0.82083333 0.79786392]
----------------------------------------
Trial 1962
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='sqrt',
n_estimators=164, random_state=100,
subsample=0.85))])
cv score: [0.7075 0.62666667 0.6925 0.80833333 0.82199367]
----------------------------------------
Trial 1963
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65333333 0.60666667 0.73333333 0.71583333 0.73971519]
----------------------------------------
Trial 1964
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=18, random_state=100,
subsample=0.9))])
cv score: [0.60833333 0.625 0.63666667 0.785 0.73971519]
----------------------------------------
Trial 1965
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.71583333 0.7975 0.85833333 0.8931962 ]
----------------------------------------
Trial 1966
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=161,
random_state=100))])
cv score: [0.76458333 0.49375 0.78708333 0.70208333 0.79232595]
----------------------------------------
Trial 1967
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=172,
random_state=100))])
cv score: [0.68666667 0.60916667 0.71833333 0.80833333 0.82199367]
----------------------------------------
Trial 1968
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.9))])
cv score: [0.6925 0.62583333 0.69583333 0.8175 0.84810127]
----------------------------------------
Trial 1969
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=117,
random_state=100))])
cv score: [0.72666667 0.68708333 0.755 0.84583333 0.86234177]
----------------------------------------
Trial 1970
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=33, random_state=100))])
cv score: [0.63541667 0.67083333 0.64875 0.81916667 0.7994462 ]
----------------------------------------
Trial 1971
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.6))])
cv score: [0.53916667 0.6225 0.58083333 0.69916667 0.62025316]
----------------------------------------
Trial 1972
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=114,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.70791667 0.76083333 0.85333333 0.87658228]
----------------------------------------
Trial 1973
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=30, random_state=100,
subsample=0.6))])
cv score: [0.65333333 0.66666667 0.7025 0.81666667 0.84414557]
----------------------------------------
Trial 1974
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7275 0.73958333 0.75791667 0.8575 0.86392405]
----------------------------------------
Trial 1975
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=180, random_state=100,
subsample=0.75))])
cv score: [0.4875 0.66666667 0.62 0.665 0.66851266]
----------------------------------------
Trial 1976
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.71583333 0.7775 0.84666667 0.88291139]
----------------------------------------
Trial 1977
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=196, random_state=100,
subsample=0.8))])
cv score: [0.68916667 0.59583333 0.69083333 0.76916667 0.77927215]
----------------------------------------
Trial 1978
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=112, random_state=100))])
cv score: [0.71375 0.61208333 0.74125 0.81166667 0.80577532]
----------------------------------------
Trial 1979
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=107,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73083333 0.68 0.77083333 0.81083333 0.86867089]
----------------------------------------
Trial 1980
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=197,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64708333 0.71625 0.74458333 0.86166667 0.83702532]
----------------------------------------
Trial 1981
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=43,
random_state=100))])
cv score: [0.54166667 0.66625 0.705 0.83 0.80696203]
----------------------------------------
Trial 1982
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=182, random_state=100,
subsample=0.65))])
cv score: [0.7 0.61166667 0.72583333 0.8025 0.81962025]
----------------------------------------
Trial 1983
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62416667 0.62 0.76083333 0.765 0.79588608]
----------------------------------------
Trial 1984
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.6))])
cv score: [0.715 0.61666667 0.69583333 0.79166667 0.8085443 ]
----------------------------------------
Trial 1985
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=32,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.8375 0.73734177]
----------------------------------------
Trial 1986
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=46,
random_state=100))])
cv score: [0.6775 0.59666667 0.72666667 0.76375 0.76938291]
----------------------------------------
Trial 1987
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=189, random_state=100))])
cv score: [0.75666667 0.67666667 0.78083333 0.83666667 0.84810127]
----------------------------------------
Trial 1988
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=22,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58125 0.69208333 0.66083333 0.82708333 0.74169304]
----------------------------------------
Trial 1989
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='sqrt',
n_estimators=94, random_state=100,
subsample=0.75))])
cv score: [0.555 0.555 0.66583333 0.62 0.75870253]
----------------------------------------
Trial 1990
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=52,
random_state=100))])
cv score: [0.69 0.50125 0.72583333 0.64958333 0.63291139]
----------------------------------------
Trial 1991
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=11,
random_state=100))])
cv score: [0.67291667 0.68125 0.69625 0.84208333 0.85205696]
----------------------------------------
Trial 1992
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71083333 0.6625 0.78583333 0.83 0.89636076]
----------------------------------------
Trial 1993
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=161,
random_state=100))])
cv score: [0.76458333 0.49375 0.78708333 0.70208333 0.79232595]
----------------------------------------
Trial 1994
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=126,
random_state=100))])
cv score: [0.69458333 0.51791667 0.8 0.66833333 0.62856013]
----------------------------------------
Trial 1995
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62666667 0.64166667 0.77166667 0.80416667 0.84098101]
----------------------------------------
Trial 1996
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.665 0.6925 0.74333333 0.78083333 0.8528481 ]
----------------------------------------
Trial 1997
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=98,
random_state=100))])
cv score: [0.69333333 0.4875 0.72166667 0.66791667 0.63370253]
----------------------------------------
Trial 1998
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
n_estimators=105, random_state=100,
subsample=0.9))])
cv score: [0.735 0.65541667 0.81791667 0.84166667 0.76107595]
----------------------------------------
Trial 1999
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='sqrt',
n_estimators=102, random_state=100,
subsample=0.8))])
cv score: [0.69 0.67166667 0.7325 0.805 0.76107595]
----------------------------------------
Trial 2000
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73166667 0.745 0.77958333 0.85458333 0.86867089]
----------------------------------------
Trial 2001
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61666667 0.64916667 0.77666667 0.75166667 0.79746835]
----------------------------------------
Trial 2002
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=64,
random_state=100))])
cv score: [0.68458333 0.51375 0.82291667 0.66791667 0.66495253]
----------------------------------------
Trial 2003
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=176,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.705 0.78833333 0.8275 0.90348101]
----------------------------------------
Trial 2004
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=144, random_state=100))])
cv score: [0.66916667 0.665 0.74083333 0.80333333 0.83781646]
----------------------------------------
Trial 2005
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.645 0.615 0.77333333 0.79416667 0.83306962]
----------------------------------------
Trial 2006
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=73,
random_state=100))])
cv score: [0.59916667 0.67291667 0.69583333 0.80416667 0.82832278]
----------------------------------------
Trial 2007
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=86, random_state=100))])
cv score: [0.71958333 0.67291667 0.77666667 0.83583333 0.83781646]
----------------------------------------
Trial 2008
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=161,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.7225 0.7925 0.85416667 0.8789557 ]
----------------------------------------
Trial 2009
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=104,
random_state=100))])
cv score: [0.68708333 0.51458333 0.82458333 0.66333333 0.66574367]
----------------------------------------
Trial 2010
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=30,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69333333 0.665 0.7325 0.7375 0.76265823]
----------------------------------------
Trial 2011
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=154, random_state=100,
subsample=0.85))])
cv score: [0.68583333 0.595 0.7225 0.80083333 0.80221519]
----------------------------------------
Trial 2012
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='sqrt',
n_estimators=134,
random_state=100))])
cv score: [0.61166667 0.58666667 0.71583333 0.72833333 0.75553797]
----------------------------------------
Trial 2013
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=114, random_state=100,
subsample=0.65))])
cv score: [0.76416667 0.67583333 0.75666667 0.8575 0.88844937]
----------------------------------------
Trial 2014
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=55, random_state=100))])
cv score: [0.66916667 0.63666667 0.76083333 0.81166667 0.8164557 ]
----------------------------------------
Trial 2015
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=187, random_state=100,
subsample=0.95))])
cv score: [0.685 0.65083333 0.71 0.80333333 0.84018987]
----------------------------------------
Trial 2016
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='sqrt',
n_estimators=183, random_state=100,
subsample=0.7))])
cv score: [0.58833333 0.6025 0.6675 0.76666667 0.77056962]
----------------------------------------
Trial 2017
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=186,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56666667 0.63833333 0.78833333 0.75 0.80142405]
----------------------------------------
Trial 2018
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.665 0.57583333 0.72916667 0.715 0.80221519]
----------------------------------------
Trial 2019
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
n_estimators=137, random_state=100,
subsample=0.7))])
cv score: [0.65416667 0.70375 0.63291667 0.8475 0.81685127]
----------------------------------------
Trial 2020
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=152,
random_state=100))])
cv score: [0.67 0.58833333 0.7175 0.81 0.82911392]
----------------------------------------
Trial 2021
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=91,
random_state=100))])
cv score: [0.67416667 0.59416667 0.68 0.80916667 0.83781646]
----------------------------------------
Trial 2022
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=127, random_state=100))])
cv score: [0.6525 0.62583333 0.74083333 0.81333333 0.83227848]
----------------------------------------
Trial 2023
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=24, random_state=100,
subsample=0.8))])
cv score: [0.72375 0.69125 0.77 0.85458333 0.87776899]
----------------------------------------
Trial 2024
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=75,
random_state=100))])
cv score: [0.69916667 0.62333333 0.67583333 0.78 0.81329114]
----------------------------------------
Trial 2025
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=170, random_state=100,
subsample=0.8))])
cv score: [0.56583333 0.64666667 0.69833333 0.66333333 0.70174051]
----------------------------------------
Trial 2026
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=31, random_state=100))])
cv score: [0.61666667 0.71625 0.69791667 0.83166667 0.89438291]
----------------------------------------
Trial 2027
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.7325 0.77666667 0.82583333 0.87341772]
----------------------------------------
Trial 2028
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=180,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.69 0.795 0.78166667 0.85917722]
----------------------------------------
Trial 2029
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7375 0.68583333 0.8075 0.8225 0.88686709]
----------------------------------------
Trial 2030
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=165, random_state=100))])
cv score: [0.69875 0.60916667 0.72333333 0.82666667 0.82120253]
----------------------------------------
Trial 2031
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=93,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 2032
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=90,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58166667 0.67083333 0.74333333 0.70833333 0.70727848]
----------------------------------------
Trial 2033
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=153, random_state=100,
subsample=0.95))])
cv score: [0.60583333 0.57 0.675 0.7375 0.75949367]
----------------------------------------
Trial 2034
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=172,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70541667 0.70458333 0.72958333 0.86 0.84335443]
----------------------------------------
Trial 2035
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=75, random_state=100,
subsample=0.7))])
cv score: [0.71 0.63083333 0.71833333 0.80833333 0.8306962 ]
----------------------------------------
Trial 2036
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=50,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7125 0.6525 0.7475 0.78833333 0.875 ]
----------------------------------------
Trial 2037
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=115,
random_state=100))])
cv score: [0.615 0.6725 0.695 0.81 0.82911392]
----------------------------------------
Trial 2038
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=10,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56333333 0.73083333 0.72875 0.83916667 0.82911392]
----------------------------------------
Trial 2039
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=198, random_state=100,
subsample=0.85))])
cv score: [0.775 0.68375 0.71791667 0.85666667 0.85759494]
----------------------------------------
Trial 2040
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=66,
random_state=100))])
cv score: [0.69166667 0.50125 0.72458333 0.65 0.63291139]
----------------------------------------
Trial 2041
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=12,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.81708333 0.7325 0.77416667 0.87333333 0.87025316]
----------------------------------------
Trial 2042
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=26, random_state=100,
subsample=0.7))])
cv score: [0.70208333 0.74375 0.67291667 0.8325 0.81052215]
----------------------------------------
Trial 2043
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=179, random_state=100,
subsample=0.8))])
cv score: [0.60083333 0.67 0.6825 0.71916667 0.56329114]
----------------------------------------
Trial 2044
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=176,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.7125 0.8 0.82916667 0.89794304]
----------------------------------------
Trial 2045
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=177,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74916667 0.7225 0.79416667 0.85833333 0.8971519 ]
----------------------------------------
Trial 2046
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=95, random_state=100))])
cv score: [0.61416667 0.70083333 0.7025 0.78833333 0.83702532]
----------------------------------------
Trial 2047
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74166667 0.74166667 0.80666667 0.86166667 0.86313291]
----------------------------------------
Trial 2048
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=59,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66666667 0.63583333 0.74 0.7925 0.80221519]
----------------------------------------
Trial 2049
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=63, random_state=100))])
cv score: [0.64916667 0.62666667 0.74916667 0.8125 0.82832278]
----------------------------------------
Trial 2050
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=103, random_state=100,
subsample=0.65))])
cv score: [0.5475 0.62916667 0.68416667 0.76583333 0.65743671]
----------------------------------------
Trial 2051
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=143,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76666667 0.71041667 0.78333333 0.84166667 0.87816456]
----------------------------------------
Trial 2052
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71583333 0.73708333 0.76875 0.84666667 0.86313291]
----------------------------------------
Trial 2053
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67833333 0.615 0.72833333 0.7 0.8125 ]
----------------------------------------
Trial 2054
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=119, random_state=100,
subsample=0.65))])
cv score: [0.51166667 0.5775 0.66166667 0.70916667 0.68117089]
----------------------------------------
Trial 2055
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=191, random_state=100,
subsample=0.95))])
cv score: [0.7525 0.63583333 0.78583333 0.81166667 0.84256329]
----------------------------------------
Trial 2056
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=129, random_state=100,
subsample=0.9))])
cv score: [0.71916667 0.60583333 0.69083333 0.80333333 0.82594937]
----------------------------------------
Trial 2057
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=61, random_state=100))])
cv score: [0.58666667 0.71541667 0.6825 0.79166667 0.82674051]
----------------------------------------
Trial 2058
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=172, random_state=100))])
cv score: [0.6975 0.60541667 0.72666667 0.82166667 0.82674051]
----------------------------------------
Trial 2059
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=66,
random_state=100))])
cv score: [0.63083333 0.59125 0.73666667 0.74875 0.79786392]
----------------------------------------
Trial 2060
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=79, random_state=100))])
cv score: [0.62291667 0.69333333 0.64333333 0.82083333 0.79984177]
----------------------------------------
Trial 2061
----------------------------------------
Parameters {'xgb__n_estimators': 56, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=56,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7875 0.73583333 0.80166667 0.85666667 0.86708861]
----------------------------------------
Trial 2062
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=170, random_state=100))])
cv score: [0.66666667 0.65666667 0.7025 0.82083333 0.8568038 ]
----------------------------------------
Trial 2063
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
n_estimators=148, random_state=100,
subsample=0.65))])
cv score: [0.73666667 0.67583333 0.79 0.83416667 0.82199367]
----------------------------------------
Trial 2064
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='sqrt',
n_estimators=54, random_state=100,
subsample=0.95))])
cv score: [0.65083333 0.635 0.69666667 0.80125 0.84295886]
----------------------------------------
Trial 2065
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=198,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74916667 0.7175 0.78666667 0.85333333 0.88607595]
----------------------------------------
Trial 2066
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=54,
random_state=100))])
cv score: [0.65208333 0.59583333 0.71791667 0.77333333 0.78441456]
----------------------------------------
Trial 2067
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=132, random_state=100,
subsample=0.85))])
cv score: [0.6575 0.57916667 0.71833333 0.7625 0.77531646]
----------------------------------------
Trial 2068
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='sqrt',
n_estimators=198, random_state=100,
subsample=0.8))])
cv score: [0.655 0.61833333 0.66666667 0.81 0.80617089]
----------------------------------------
Trial 2069
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=82, random_state=100,
subsample=0.6))])
cv score: [0.47416667 0.6575 0.62833333 0.67416667 0.66218354]
----------------------------------------
Trial 2070
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=148, random_state=100,
subsample=0.75))])
cv score: [0.59416667 0.65916667 0.58916667 0.70916667 0.76977848]
----------------------------------------
Trial 2071
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=71, random_state=100,
subsample=0.65))])
cv score: [0.57416667 0.58916667 0.6975 0.66333333 0.72310127]
----------------------------------------
Trial 2072
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=170,
random_state=100,
subsample=0.85))])
cv score: [0.71166667 0.61583333 0.7775 0.81916667 0.80775316]
----------------------------------------
Trial 2073
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
n_estimators=174, random_state=100,
subsample=0.9))])
cv score: [0.605 0.63 0.765 0.82 0.78243671]
----------------------------------------
Trial 2074
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=145,
random_state=100))])
cv score: [0.53875 0.67041667 0.71375 0.82666667 0.78401899]
----------------------------------------
Trial 2075
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=74, random_state=100,
subsample=0.95))])
cv score: [0.735 0.61541667 0.79333333 0.81083333 0.80063291]
----------------------------------------
Trial 2076
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=118,
random_state=100))])
cv score: [0.7 0.60083333 0.6775 0.80333333 0.83781646]
----------------------------------------
Trial 2077
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.8))])
cv score: [0.6725 0.64916667 0.71583333 0.8125 0.83544304]
----------------------------------------
Trial 2078
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=19,
random_state=100))])
cv score: [0.68916667 0.4875 0.72208333 0.67041667 0.6380538 ]
----------------------------------------
Trial 2079
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=147,
random_state=100))])
cv score: [0.69083333 0.63083333 0.69916667 0.805 0.82753165]
----------------------------------------
Trial 2080
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=20, random_state=100))])
cv score: [0.70333333 0.61583333 0.73 0.69333333 0.80300633]
----------------------------------------
Trial 2081
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=61, random_state=100))])
cv score: [0.74666667 0.69666667 0.73833333 0.8525 0.88924051]
----------------------------------------
Trial 2082
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=175,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7825 0.73583333 0.78916667 0.8425 0.87183544]
----------------------------------------
Trial 2083
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
n_estimators=164, random_state=100,
subsample=0.95))])
cv score: [0.72083333 0.6925 0.75416667 0.81333333 0.86867089]
----------------------------------------
Trial 2084
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=107,
random_state=100,
subsample=0.65))])
cv score: [0.73583333 0.66583333 0.76916667 0.80333333 0.80617089]
----------------------------------------
Trial 2085
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74583333 0.69333333 0.795 0.8 0.85047468]
----------------------------------------
Trial 2086
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=179, random_state=100))])
cv score: [0.78666667 0.68666667 0.7625 0.84416667 0.87579114]
----------------------------------------
Trial 2087
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=171, random_state=100))])
cv score: [0.77916667 0.69666667 0.775 0.8525 0.87025316]
----------------------------------------
Trial 2088
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
n_estimators=84, random_state=100,
subsample=0.8))])
cv score: [0.65583333 0.59666667 0.7025 0.75916667 0.81724684]
----------------------------------------
Trial 2089
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=143,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.695 0.8025 0.82333333 0.89082278]
----------------------------------------
Trial 2090
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6575 0.66583333 0.72916667 0.75833333 0.85047468]
----------------------------------------
Trial 2091
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=163,
random_state=100))])
cv score: [0.70333333 0.6025 0.675 0.80166667 0.84177215]
----------------------------------------
Trial 2092
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=75, random_state=100))])
cv score: [0.69083333 0.64 0.745 0.77916667 0.82278481]
----------------------------------------
Trial 2093
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='log2',
n_estimators=179, random_state=100,
subsample=0.95))])
cv score: [0.55958333 0.6675 0.65291667 0.78708333 0.78797468]
----------------------------------------
Trial 2094
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.65))])
cv score: [0.6875 0.64916667 0.71333333 0.8225 0.82436709]
----------------------------------------
Trial 2095
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=137, random_state=100))])
cv score: [0.68916667 0.65666667 0.72583333 0.81333333 0.85917722]
----------------------------------------
Trial 2096
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59708333 0.66333333 0.64625 0.80208333 0.76621835]
----------------------------------------
Trial 2097
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=20, random_state=100,
subsample=0.7))])
cv score: [0.51083333 0.585 0.60416667 0.59583333 0.78322785]
----------------------------------------
Trial 2098
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='sqrt',
n_estimators=187, random_state=100,
subsample=0.8))])
cv score: [0.64916667 0.54416667 0.69 0.76416667 0.7056962 ]
----------------------------------------
Trial 2099
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=74, random_state=100))])
cv score: [0.61 0.68333333 0.71083333 0.7825 0.82436709]
----------------------------------------
Trial 2100
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=113,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66416667 0.655 0.7875 0.72416667 0.83781646]
----------------------------------------
Trial 2101
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=90,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71583333 0.68583333 0.77833333 0.78083333 0.8568038 ]
----------------------------------------
Trial 2102
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=32,
random_state=100))])
cv score: [0.52333333 0.675 0.71916667 0.8375 0.80577532]
----------------------------------------
Trial 2103
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75 0.72583333 0.8075 0.825 0.88291139]
----------------------------------------
Trial 2104
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=156,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 2105
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=76,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.7325 0.78916667 0.84333333 0.87579114]
----------------------------------------
Trial 2106
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=48, random_state=100))])
cv score: [0.74083333 0.67 0.77666667 0.84083333 0.85917722]
----------------------------------------
Trial 2107
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=61,
random_state=100))])
cv score: [0.6075 0.67208333 0.69333333 0.81 0.84256329]
----------------------------------------
Trial 2108
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=14, random_state=100,
subsample=0.9))])
cv score: [0.66416667 0.54083333 0.6825 0.75541667 0.80775316]
----------------------------------------
Trial 2109
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=158, random_state=100))])
cv score: [0.69333333 0.6525 0.715 0.7975 0.83544304]
----------------------------------------
Trial 2110
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
n_estimators=162, random_state=100,
subsample=0.7))])
cv score: [0.7225 0.7075 0.75916667 0.78 0.78006329]
----------------------------------------
Trial 2111
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=144, random_state=100))])
cv score: [0.66916667 0.665 0.74083333 0.80333333 0.83781646]
----------------------------------------
Trial 2112
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=29,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68083333 0.63916667 0.68083333 0.715 0.74367089]
----------------------------------------
Trial 2113
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=129,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73833333 0.7075 0.81333333 0.83083333 0.91297468]
----------------------------------------
Trial 2114
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
n_estimators=112, random_state=100,
subsample=0.85))])
cv score: [0.72333333 0.64791667 0.8225 0.83583333 0.79825949]
----------------------------------------
Trial 2115
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=53, random_state=100))])
cv score: [0.61166667 0.67666667 0.71041667 0.80083333 0.83386076]
----------------------------------------
Trial 2116
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=112,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62333333 0.59916667 0.79083333 0.7275 0.85601266]
----------------------------------------
Trial 2117
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66083333 0.665 0.79416667 0.78 0.87816456]
----------------------------------------
Trial 2118
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=188,
random_state=100))])
cv score: [0.60333333 0.3775 0.73791667 0.595 0.63291139]
----------------------------------------
Trial 2119
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=154, random_state=100))])
cv score: [0.665 0.65583333 0.7 0.82 0.85522152]
----------------------------------------
Trial 2120
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='log2',
n_estimators=171, random_state=100,
subsample=0.9))])
cv score: [0.51833333 0.54083333 0.73 0.67916667 0.73496835]
----------------------------------------
Trial 2121
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=176,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75833333 0.72 0.78083333 0.845 0.87183544]
----------------------------------------
Trial 2122
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=153, random_state=100))])
cv score: [0.61666667 0.69166667 0.70333333 0.79916667 0.83702532]
----------------------------------------
Trial 2123
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=190,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62583333 0.64416667 0.78166667 0.76583333 0.83227848]
----------------------------------------
Trial 2124
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=193,
random_state=100))])
cv score: [0.68708333 0.49375 0.81 0.64833333 0.75316456]
----------------------------------------
Trial 2125
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='log2',
n_estimators=27, random_state=100,
subsample=0.9))])
cv score: [0.57791667 0.63916667 0.68791667 0.84166667 0.83583861]
----------------------------------------
Trial 2126
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=192, random_state=100,
subsample=0.8))])
cv score: [0.67083333 0.59583333 0.72 0.81416667 0.79667722]
----------------------------------------
Trial 2127
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=174, random_state=100))])
cv score: [0.69833333 0.63416667 0.72166667 0.80666667 0.83306962]
----------------------------------------
Trial 2128
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=30,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83541667 0.82199367]
----------------------------------------
Trial 2129
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=103,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5425 0.6125 0.68583333 0.68416667 0.70332278]
----------------------------------------
Trial 2130
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='sqrt',
n_estimators=35, random_state=100,
subsample=0.95))])
cv score: [0.68333333 0.58541667 0.7 0.80333333 0.84493671]
----------------------------------------
Trial 2131
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=187,
random_state=100))])
cv score: [0.68166667 0.5925 0.705 0.80583333 0.81012658]
----------------------------------------
Trial 2132
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=52, random_state=100))])
cv score: [0.61916667 0.66416667 0.7175 0.77416667 0.82515823]
----------------------------------------
Trial 2133
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65083333 0.6375 0.75833333 0.73666667 0.80696203]
----------------------------------------
Trial 2134
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61166667 0.64833333 0.7575 0.73333333 0.83544304]
----------------------------------------
Trial 2135
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=193, random_state=100,
subsample=0.75))])
cv score: [0.68583333 0.6375 0.72583333 0.79833333 0.79905063]
----------------------------------------
Trial 2136
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=133, random_state=100,
subsample=0.9))])
cv score: [0.53583333 0.5975 0.68 0.765 0.79905063]
----------------------------------------
Trial 2137
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=58,
random_state=100))])
cv score: [0.6125 0.60083333 0.73958333 0.74666667 0.81091772]
----------------------------------------
Trial 2138
----------------------------------------
Parameters {'xgb__n_estimators': 56, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=56,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6975 0.68083333 0.76 0.765 0.84414557]
----------------------------------------
Trial 2139
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=59, random_state=100))])
cv score: [0.62833333 0.67416667 0.735 0.78083333 0.81803797]
----------------------------------------
Trial 2140
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='log2',
n_estimators=101, random_state=100,
subsample=0.7))])
cv score: [0.61833333 0.65916667 0.6825 0.7525 0.83148734]
----------------------------------------
Trial 2141
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.735 0.73083333 0.7475 0.87 0.85047468]
----------------------------------------
Trial 2142
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=54,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.74333333 0.7725 0.83416667 0.87658228]
----------------------------------------
Trial 2143
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=139,
random_state=100))])
cv score: [0.68708333 0.49375 0.80833333 0.64625 0.75316456]
----------------------------------------
Trial 2144
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62583333 0.62333333 0.79083333 0.73333333 0.7721519 ]
----------------------------------------
Trial 2145
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=16,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.72833333 0.67625 0.84666667 0.8568038 ]
----------------------------------------
Trial 2146
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70125 0.72416667 0.76458333 0.87 0.85917722]
----------------------------------------
Trial 2147
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=116, random_state=100))])
cv score: [0.76083333 0.69 0.78916667 0.8375 0.85522152]
----------------------------------------
Trial 2148
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=155,
random_state=100))])
cv score: [0.69 0.63 0.69833333 0.7975 0.82674051]
----------------------------------------
Trial 2149
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=164, random_state=100))])
cv score: [0.68291667 0.63416667 0.74666667 0.82416667 0.81803797]
----------------------------------------
Trial 2150
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69583333 0.7325 0.76 0.865 0.85917722]
----------------------------------------
Trial 2151
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=74, random_state=100))])
cv score: [0.65833333 0.65416667 0.74333333 0.79333333 0.82436709]
----------------------------------------
Trial 2152
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.74083333 0.75791667 0.85 0.875 ]
----------------------------------------
Trial 2153
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.64333333 0.76583333 0.725 0.74208861]
----------------------------------------
Trial 2154
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=54, random_state=100))])
cv score: [0.75416667 0.6975 0.7475 0.8525 0.88370253]
----------------------------------------
Trial 2155
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=35,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74083333 0.72625 0.79416667 0.8225 0.85917722]
----------------------------------------
Trial 2156
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=57,
random_state=100))])
cv score: [0.5625 0.66208333 0.685 0.82458333 0.83623418]
----------------------------------------
Trial 2157
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=57,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70833333 0.67833333 0.77333333 0.7725 0.83227848]
----------------------------------------
Trial 2158
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=23, random_state=100))])
cv score: [0.69083333 0.60583333 0.7425 0.75 0.80696203]
----------------------------------------
Trial 2159
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.67916667 0.78083333 0.8 0.84335443]
----------------------------------------
Trial 2160
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76 0.73 0.77666667 0.83833333 0.92088608]
----------------------------------------
Trial 2161
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=53, random_state=100))])
cv score: [0.62166667 0.6625 0.71583333 0.78916667 0.82436709]
----------------------------------------
Trial 2162
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=35, random_state=100))])
cv score: [0.61791667 0.63 0.75666667 0.83125 0.79628165]
----------------------------------------
Trial 2163
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=38, random_state=100))])
cv score: [0.65541667 0.66291667 0.7125 0.81916667 0.8125 ]
----------------------------------------
Trial 2164
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=104,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74083333 0.7375 0.77708333 0.84583333 0.8710443 ]
----------------------------------------
Trial 2165
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=193, random_state=100,
subsample=0.9))])
cv score: [0.68833333 0.55416667 0.65916667 0.72916667 0.76265823]
----------------------------------------
Trial 2166
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.695 0.7775 0.80333333 0.83306962]
----------------------------------------
Trial 2167
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
n_estimators=169, random_state=100,
subsample=0.8))])
cv score: [0.735 0.70166667 0.71583333 0.77333333 0.65664557]
----------------------------------------
Trial 2168
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=107,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.74166667 0.77583333 0.86333333 0.84572785]
----------------------------------------
Trial 2169
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='sqrt',
n_estimators=107,
random_state=100))])
cv score: [0.69916667 0.60166667 0.70833333 0.77666667 0.81012658]
----------------------------------------
Trial 2170
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=36,
random_state=100))])
cv score: [0.62333333 0.65208333 0.68166667 0.82541667 0.8306962 ]
----------------------------------------
Trial 2171
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
n_estimators=176, random_state=100,
subsample=0.7))])
cv score: [0.63083333 0.65416667 0.72083333 0.79583333 0.77373418]
----------------------------------------
Trial 2172
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=91,
random_state=100,
subsample=0.85))])
cv score: [0.71166667 0.6375 0.80916667 0.82666667 0.81487342]
----------------------------------------
Trial 2173
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='log2',
n_estimators=12, random_state=100,
subsample=0.7))])
cv score: [0.57416667 0.6525 0.7425 0.73916667 0.78639241]
----------------------------------------
Trial 2174
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='sqrt',
n_estimators=66, random_state=100,
subsample=0.65))])
cv score: [0.68583333 0.55 0.6625 0.76416667 0.68037975]
----------------------------------------
Trial 2175
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=44, random_state=100,
subsample=0.8))])
cv score: [0.68416667 0.66416667 0.72666667 0.81166667 0.79905063]
----------------------------------------
Trial 2176
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.6))])
cv score: [0.71666667 0.66583333 0.73583333 0.80416667 0.80696203]
----------------------------------------
Trial 2177
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=130,
random_state=100))])
cv score: [0.7275 0.58833333 0.73666667 0.81 0.84018987]
----------------------------------------
Trial 2178
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=71,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.72416667 0.78583333 0.81916667 0.89794304]
----------------------------------------
Trial 2179
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=84,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72 0.685 0.77916667 0.82166667 0.91139241]
----------------------------------------
Trial 2180
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=173,
random_state=100))])
cv score: [0.66791667 0.58583333 0.72125 0.805 0.80063291]
----------------------------------------
Trial 2181
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=70,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72083333 0.76041667 0.77625 0.86041667 0.84810127]
----------------------------------------
Trial 2182
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='log2',
n_estimators=156, random_state=100,
subsample=0.75))])
cv score: [0.6825 0.64 0.7025 0.81583333 0.83623418]
----------------------------------------
Trial 2183
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=91, random_state=100))])
cv score: [0.76 0.69166667 0.74416667 0.85833333 0.8971519 ]
----------------------------------------
Trial 2184
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=106, random_state=100))])
cv score: [0.71958333 0.68083333 0.78333333 0.84208333 0.83781646]
----------------------------------------
Trial 2185
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='log2',
n_estimators=94, random_state=100,
subsample=0.75))])
cv score: [0.58 0.63833333 0.665 0.68583333 0.75712025]
----------------------------------------
Trial 2186
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58916667 0.72833333 0.76833333 0.81 0.86669304]
----------------------------------------
Trial 2187
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=17, random_state=100))])
cv score: [0.74666667 0.69875 0.73583333 0.86416667 0.8710443 ]
----------------------------------------
Trial 2188
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='log2',
n_estimators=37, random_state=100,
subsample=0.8))])
cv score: [0.61166667 0.66625 0.68291667 0.83208333 0.78481013]
----------------------------------------
Trial 2189
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=12, random_state=100,
subsample=0.85))])
cv score: [0.66583333 0.65041667 0.73166667 0.8525 0.78876582]
----------------------------------------
Trial 2190
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7375 0.705 0.76791667 0.8475 0.86867089]
----------------------------------------
Trial 2191
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=86,
random_state=100))])
cv score: [0.67666667 0.60416667 0.71083333 0.81333333 0.82199367]
----------------------------------------
Trial 2192
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=80,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.72833333 0.78 0.86583333 0.88053797]
----------------------------------------
Trial 2193
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=102, random_state=100))])
cv score: [0.69916667 0.62916667 0.7225 0.81083333 0.82753165]
----------------------------------------
Trial 2194
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=135, random_state=100))])
cv score: [0.75666667 0.67958333 0.7875 0.8325 0.84731013]
----------------------------------------
Trial 2195
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=141, random_state=100))])
cv score: [0.6775 0.6225 0.735 0.8025 0.82832278]
----------------------------------------
Trial 2196
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=137,
random_state=100))])
cv score: [0.51541667 0.67958333 0.70791667 0.82416667 0.77373418]
----------------------------------------
Trial 2197
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=105, random_state=100))])
cv score: [0.67833333 0.6425 0.72166667 0.80166667 0.83939873]
----------------------------------------
Trial 2198
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=110, random_state=100,
subsample=0.65))])
cv score: [0.62333333 0.69833333 0.70833333 0.82916667 0.82199367]
----------------------------------------
Trial 2199
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='log2',
n_estimators=196, random_state=100,
subsample=0.9))])
cv score: [0.6875 0.60166667 0.67166667 0.785 0.7721519 ]
----------------------------------------
Trial 2200
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.73416667 0.76291667 0.84916667 0.86155063]
----------------------------------------
Trial 2201
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=59, random_state=100,
subsample=0.85))])
cv score: [0.6425 0.55166667 0.71583333 0.78916667 0.7278481 ]
----------------------------------------
Trial 2202
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='log2',
n_estimators=40, random_state=100,
subsample=0.85))])
cv score: [0.65916667 0.59916667 0.68333333 0.69 0.74525316]
----------------------------------------
Trial 2203
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73083333 0.66333333 0.77 0.80583333 0.86787975]
----------------------------------------
Trial 2204
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=135,
random_state=100, subsample=0.8))])
cv score: [0.68083333 0.66 0.75083333 0.7775 0.61234177]
----------------------------------------
Trial 2205
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=190,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7575 0.72666667 0.76916667 0.86166667 0.87025316]
----------------------------------------
Trial 2206
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=187, random_state=100))])
cv score: [0.66416667 0.65166667 0.7375 0.80416667 0.84256329]
----------------------------------------
Trial 2207
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.68166667 0.7825 0.81 0.90664557]
----------------------------------------
Trial 2208
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=115,
random_state=100))])
cv score: [0.65583333 0.58916667 0.69833333 0.80875 0.81566456]
----------------------------------------
Trial 2209
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='log2',
n_estimators=67, random_state=100,
subsample=0.8))])
cv score: [0.67833333 0.61083333 0.75 0.78083333 0.78639241]
----------------------------------------
Trial 2210
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=189,
random_state=100))])
cv score: [0.645 0.60833333 0.71 0.77416667 0.78164557]
----------------------------------------
Trial 2211
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=16,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63458333 0.72166667 0.77375 0.85083333 0.86511076]
----------------------------------------
Trial 2212
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6325 0.61583333 0.7125 0.70458333 0.73575949]
----------------------------------------
Trial 2213
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=44, random_state=100,
subsample=0.6))])
cv score: [0.62583333 0.66583333 0.66 0.81833333 0.81487342]
----------------------------------------
Trial 2214
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.74416667 0.80666667 0.86583333 0.86313291]
----------------------------------------
Trial 2215
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=28, random_state=100))])
cv score: [0.76625 0.6925 0.74541667 0.85458333 0.88291139]
----------------------------------------
Trial 2216
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.68416667 0.785 0.78666667 0.87658228]
----------------------------------------
Trial 2217
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69125 0.75375 0.77166667 0.83791667 0.8710443 ]
----------------------------------------
Trial 2218
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=105,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83625 0.82199367]
----------------------------------------
Trial 2219
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=104, random_state=100))])
cv score: [0.74333333 0.68875 0.71916667 0.85666667 0.87420886]
----------------------------------------
Trial 2220
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=14, random_state=100))])
cv score: [0.7125 0.64916667 0.77125 0.86333333 0.81803797]
----------------------------------------
Trial 2221
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=69,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76875 0.745 0.77791667 0.85 0.87262658]
----------------------------------------
Trial 2222
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='log2',
n_estimators=101, random_state=100,
subsample=0.95))])
cv score: [0.67833333 0.57083333 0.66416667 0.7875 0.7903481 ]
----------------------------------------
Trial 2223
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=49, random_state=100))])
cv score: [0.655 0.61541667 0.73583333 0.81791667 0.78560127]
----------------------------------------
Trial 2224
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=152,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75 0.72333333 0.7975 0.86 0.88844937]
----------------------------------------
Trial 2225
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=105,
random_state=100))])
cv score: [0.67583333 0.6025 0.7175 0.80916667 0.81803797]
----------------------------------------
Trial 2226
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=151,
random_state=100,
subsample=0.85))])
cv score: [0.7175 0.58666667 0.78083333 0.76166667 0.70015823]
----------------------------------------
Trial 2227
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=75, random_state=100))])
cv score: [0.765 0.69416667 0.78166667 0.83416667 0.85838608]
----------------------------------------
Trial 2228
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=52, random_state=100))])
cv score: [0.71041667 0.66916667 0.77875 0.82916667 0.84375 ]
----------------------------------------
Trial 2229
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=137, random_state=100,
subsample=0.75))])
cv score: [0.56666667 0.68583333 0.655 0.76916667 0.66455696]
----------------------------------------
Trial 2230
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='sqrt',
n_estimators=78, random_state=100,
subsample=0.6))])
cv score: [0.72833333 0.68 0.745 0.8 0.81170886]
----------------------------------------
Trial 2231
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=74,
random_state=100))])
cv score: [0.59916667 0.67041667 0.695 0.80666667 0.82832278]
----------------------------------------
Trial 2232
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=38, random_state=100,
subsample=0.85))])
cv score: [0.67333333 0.63541667 0.81083333 0.82416667 0.76977848]
----------------------------------------
Trial 2233
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=96,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61666667 0.6375 0.76166667 0.78666667 0.82832278]
----------------------------------------
Trial 2234
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=99, random_state=100))])
cv score: [0.70166667 0.62416667 0.72875 0.80833333 0.79707278]
----------------------------------------
Trial 2235
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=48, random_state=100,
subsample=0.9))])
cv score: [0.70666667 0.63 0.73833333 0.66666667 0.70015823]
----------------------------------------
Trial 2236
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=22, random_state=100,
subsample=0.75))])
cv score: [0.68916667 0.48166667 0.59416667 0.4875 0.63528481]
----------------------------------------
Trial 2237
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=23, random_state=100,
subsample=0.9))])
cv score: [0.67583333 0.515 0.64416667 0.66 0.85126582]
----------------------------------------
Trial 2238
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=181, random_state=100,
subsample=0.8))])
cv score: [0.61166667 0.5425 0.615 0.70166667 0.625 ]
----------------------------------------
Trial 2239
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=90,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78916667 0.74333333 0.79416667 0.86916667 0.86075949]
----------------------------------------
Trial 2240
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65416667 0.62 0.77333333 0.7075 0.77136076]
----------------------------------------
Trial 2241
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=50,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71541667 0.72375 0.76916667 0.8575 0.86075949]
----------------------------------------
Trial 2242
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=107, random_state=100))])
cv score: [0.70416667 0.60208333 0.73166667 0.82625 0.8085443 ]
----------------------------------------
Trial 2243
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=145, random_state=100))])
cv score: [0.69833333 0.61916667 0.725 0.80333333 0.84651899]
----------------------------------------
Trial 2244
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
n_estimators=165,
random_state=100))])
cv score: [0.74958333 0.66041667 0.74416667 0.83 0.89438291]
----------------------------------------
Trial 2245
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=110,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6225 0.61333333 0.75833333 0.7675 0.82594937]
----------------------------------------
Trial 2246
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=195,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67666667 0.66083333 0.77583333 0.795 0.86075949]
----------------------------------------
Trial 2247
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=88,
random_state=100))])
cv score: [0.64416667 0.585 0.70458333 0.8075 0.82041139]
----------------------------------------
Trial 2248
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.55958333 0.67833333 0.69541667 0.8025 0.79549051]
----------------------------------------
Trial 2249
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=80, random_state=100,
subsample=0.95))])
cv score: [0.65166667 0.5825 0.6525 0.785 0.78481013]
----------------------------------------
Trial 2250
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=89, random_state=100,
subsample=0.85))])
cv score: [0.78458333 0.66291667 0.75375 0.83333333 0.85126582]
----------------------------------------
Trial 2251
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=170,
random_state=100))])
cv score: [0.585 0.66583333 0.7025 0.8325 0.8306962 ]
----------------------------------------
Trial 2252
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=17,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7225 0.71666667 0.80083333 0.795 0.85363924]
----------------------------------------
Trial 2253
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
n_estimators=130, random_state=100,
subsample=0.8))])
cv score: [0.60416667 0.58833333 0.6525 0.74666667 0.73892405]
----------------------------------------
Trial 2254
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=180, random_state=100,
subsample=0.75))])
cv score: [0.47916667 0.54583333 0.57666667 0.75833333 0.73892405]
----------------------------------------
Trial 2255
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73 0.74125 0.78958333 0.85333333 0.86946203]
----------------------------------------
Trial 2256
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=81,
random_state=100))])
cv score: [0.69458333 0.51791667 0.79916667 0.66958333 0.62856013]
----------------------------------------
Trial 2257
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=170, random_state=100))])
cv score: [0.74083333 0.68291667 0.7275 0.8575 0.88607595]
----------------------------------------
Trial 2258
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=92, random_state=100,
subsample=0.9))])
cv score: [0.68833333 0.565 0.71166667 0.82666667 0.78955696]
----------------------------------------
Trial 2259
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75 0.72875 0.7875 0.8525 0.88212025]
----------------------------------------
Trial 2260
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=132, random_state=100,
subsample=0.6))])
cv score: [0.5675 0.55583333 0.60583333 0.60416667 0.65585443]
----------------------------------------
Trial 2261
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=47, random_state=100))])
cv score: [0.745 0.6675 0.77416667 0.84583333 0.85917722]
----------------------------------------
Trial 2262
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.67916667 0.7825 0.76166667 0.85838608]
----------------------------------------
Trial 2263
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=92, random_state=100))])
cv score: [0.7475 0.6775 0.77833333 0.8325 0.84572785]
----------------------------------------
Trial 2264
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=178,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.70208333 0.76083333 0.85541667 0.85759494]
----------------------------------------
Trial 2265
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.67666667 0.75666667 0.79166667 0.87816456]
----------------------------------------
Trial 2266
----------------------------------------
Parameters {'rf__n_estimators': 125, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=125,
random_state=100))])
cv score: [0.6925 0.59583333 0.6775 0.80833333 0.83781646]
----------------------------------------
Trial 2267
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=131,
random_state=100))])
cv score: [0.68666667 0.62083333 0.68583333 0.79833333 0.81408228]
----------------------------------------
Trial 2268
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=117, random_state=100))])
cv score: [0.68083333 0.63916667 0.72833333 0.80416667 0.84177215]
----------------------------------------
Trial 2269
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=41,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74458333 0.72541667 0.78583333 0.87125 0.89992089]
----------------------------------------
Trial 2270
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53 0.68416667 0.64375 0.79708333 0.79786392]
----------------------------------------
Trial 2271
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=145, random_state=100))])
cv score: [0.57333333 0.71583333 0.6925 0.80583333 0.79905063]
----------------------------------------
Trial 2272
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=110,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72666667 0.68416667 0.79583333 0.80333333 0.87420886]
----------------------------------------
Trial 2273
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=124, random_state=100))])
cv score: [0.7175 0.68791667 0.7875 0.83416667 0.83939873]
----------------------------------------
Trial 2274
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=158, random_state=100,
subsample=0.95))])
cv score: [0.65416667 0.60416667 0.65333333 0.77666667 0.68433544]
----------------------------------------
Trial 2275
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=83, random_state=100))])
cv score: [0.56083333 0.73208333 0.68916667 0.79166667 0.80063291]
----------------------------------------
Trial 2276
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70291667 0.74125 0.76791667 0.85833333 0.86155063]
----------------------------------------
Trial 2277
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=197, random_state=100,
subsample=0.7))])
cv score: [0.45333333 0.59166667 0.59166667 0.63916667 0.70490506]
----------------------------------------
Trial 2278
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=144,
random_state=100))])
cv score: [0.76458333 0.49375 0.78708333 0.70291667 0.79232595]
----------------------------------------
Trial 2279
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=184,
random_state=100))])
cv score: [0.67416667 0.62333333 0.705 0.80166667 0.8306962 ]
----------------------------------------
Trial 2280
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=196, random_state=100,
subsample=0.65))])
cv score: [0.56666667 0.57583333 0.53583333 0.64416667 0.65348101]
----------------------------------------
Trial 2281
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='log2',
n_estimators=142, random_state=100,
subsample=0.65))])
cv score: [0.65916667 0.6825 0.67833333 0.76666667 0.75158228]
----------------------------------------
Trial 2282
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65416667 0.66583333 0.76583333 0.7375 0.83623418]
----------------------------------------
Trial 2283
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=195, random_state=100))])
cv score: [0.69 0.6125 0.72833333 0.82083333 0.82357595]
----------------------------------------
Trial 2284
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=28, random_state=100,
subsample=0.85))])
cv score: [0.68583333 0.64708333 0.83375 0.80083333 0.74762658]
----------------------------------------
Trial 2285
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
n_estimators=124, random_state=100,
subsample=0.8))])
cv score: [0.76583333 0.705 0.75083333 0.85833333 0.88686709]
----------------------------------------
Trial 2286
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=78, random_state=100,
subsample=0.8))])
cv score: [0.74833333 0.70833333 0.7475 0.86 0.84295886]
----------------------------------------
Trial 2287
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=127, random_state=100))])
cv score: [0.64833333 0.67333333 0.74 0.79083333 0.83860759]
----------------------------------------
Trial 2288
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68125 0.71333333 0.79125 0.8725 0.88370253]
----------------------------------------
Trial 2289
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.7025 0.73833333 0.74833333 0.84493671]
----------------------------------------
Trial 2290
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76916667 0.73375 0.78208333 0.83 0.86629747]
----------------------------------------
Trial 2291
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=46,
random_state=100))])
cv score: [0.6925 0.51791667 0.79958333 0.67041667 0.6289557 ]
----------------------------------------
Trial 2292
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78041667 0.74416667 0.77416667 0.85166667 0.85996835]
----------------------------------------
Trial 2293
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=42,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59 0.72041667 0.70875 0.845 0.83900316]
----------------------------------------
Trial 2294
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=71,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.73375 0.73541667 0.85666667 0.84651899]
----------------------------------------
Trial 2295
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=67,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75666667 0.69 0.76916667 0.80333333 0.92088608]
----------------------------------------
Trial 2296
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7825 0.73583333 0.79208333 0.84916667 0.8710443 ]
----------------------------------------
Trial 2297
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
n_estimators=123, random_state=100,
subsample=0.6))])
cv score: [0.72916667 0.69083333 0.76833333 0.84166667 0.84335443]
----------------------------------------
Trial 2298
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64333333 0.645 0.7675 0.77333333 0.83939873]
----------------------------------------
Trial 2299
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=80,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68916667 0.64583333 0.79416667 0.735 0.84177215]
----------------------------------------
Trial 2300
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61083333 0.6375 0.75583333 0.735 0.81724684]
----------------------------------------
Trial 2301
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='sqrt',
n_estimators=21, random_state=100,
subsample=0.85))])
cv score: [0.70416667 0.60916667 0.69416667 0.725 0.73259494]
----------------------------------------
Trial 2302
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=92,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.475 0.5675 0.5725 0.84791667 0.70846519]
----------------------------------------
Trial 2303
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.67833333 0.76416667 0.80083333 0.86471519]
----------------------------------------
Trial 2304
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='log2',
n_estimators=138, random_state=100,
subsample=0.7))])
cv score: [0.515 0.6975 0.7125 0.61916667 0.70727848]
----------------------------------------
Trial 2305
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=111, random_state=100,
subsample=0.75))])
cv score: [0.655 0.67833333 0.68583333 0.83083333 0.8085443 ]
----------------------------------------
Trial 2306
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74166667 0.74 0.76916667 0.85833333 0.86234177]
----------------------------------------
Trial 2307
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=54, random_state=100,
subsample=0.75))])
cv score: [0.63166667 0.6075 0.70416667 0.81833333 0.80537975]
----------------------------------------
Trial 2308
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=119, random_state=100,
subsample=0.95))])
cv score: [0.69416667 0.56083333 0.70666667 0.76333333 0.83148734]
----------------------------------------
Trial 2309
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=15,
random_state=100))])
cv score: [0.54791667 0.63375 0.64833333 0.84333333 0.82001582]
----------------------------------------
Trial 2310
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
n_estimators=197, random_state=100,
subsample=0.85))])
cv score: [0.69583333 0.645 0.76666667 0.8075 0.81012658]
----------------------------------------
Trial 2311
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.73833333 0.77333333 0.86 0.86629747]
----------------------------------------
Trial 2312
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=161,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76416667 0.74 0.79666667 0.86666667 0.86708861]
----------------------------------------
Trial 2313
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.72666667 0.8075 0.85083333 0.8971519 ]
----------------------------------------
Trial 2314
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=70, random_state=100,
subsample=0.7))])
cv score: [0.63833333 0.57583333 0.7 0.64916667 0.70015823]
----------------------------------------
Trial 2315
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=111,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83666667 0.82199367]
----------------------------------------
Trial 2316
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='log2',
n_estimators=49, random_state=100,
subsample=0.75))])
cv score: [0.66833333 0.665 0.70416667 0.815 0.79984177]
----------------------------------------
Trial 2317
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=99, random_state=100))])
cv score: [0.7475 0.69041667 0.7125 0.855 0.87658228]
----------------------------------------
Trial 2318
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=101, random_state=100,
subsample=0.65))])
cv score: [0.6625 0.665 0.7325 0.81083333 0.85601266]
----------------------------------------
Trial 2319
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=166,
random_state=100))])
cv score: [0.68333333 0.5925 0.70166667 0.79333333 0.81012658]
----------------------------------------
Trial 2320
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=157, random_state=100))])
cv score: [0.74666667 0.68708333 0.7275 0.85583333 0.88528481]
----------------------------------------
Trial 2321
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=126, random_state=100))])
cv score: [0.61416667 0.7 0.70583333 0.7925 0.82278481]
----------------------------------------
Trial 2322
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=175,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.65 0.77416667 0.75583333 0.8346519 ]
----------------------------------------
Trial 2323
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=78,
random_state=100))])
cv score: [0.63583333 0.59333333 0.72208333 0.77625 0.77333861]
----------------------------------------
Trial 2324
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=152, random_state=100))])
cv score: [0.6175 0.69083333 0.70416667 0.79833333 0.83623418]
----------------------------------------
Trial 2325
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68416667 0.68 0.77416667 0.76583333 0.89873418]
----------------------------------------
Trial 2326
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=184, random_state=100))])
cv score: [0.75583333 0.68 0.78333333 0.83583333 0.85047468]
----------------------------------------
Trial 2327
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77916667 0.75166667 0.79916667 0.85333333 0.85759494]
----------------------------------------
Trial 2328
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='sqrt',
n_estimators=44, random_state=100,
subsample=0.6))])
cv score: [0.59416667 0.72416667 0.67416667 0.805 0.87341772]
----------------------------------------
Trial 2329
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=188, random_state=100,
subsample=0.6))])
cv score: [0.56166667 0.59083333 0.615 0.68666667 0.62183544]
----------------------------------------
Trial 2330
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=103, random_state=100))])
cv score: [0.69208333 0.63958333 0.72708333 0.835 0.81843354]
----------------------------------------
Trial 2331
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.6075 0.72375 0.72333333 0.7460443 ]
----------------------------------------
Trial 2332
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='log2',
n_estimators=148, random_state=100,
subsample=0.7))])
cv score: [0.65916667 0.595 0.69916667 0.79416667 0.81408228]
----------------------------------------
Trial 2333
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=101, random_state=100,
subsample=0.85))])
cv score: [0.68583333 0.6125 0.64666667 0.81 0.79825949]
----------------------------------------
Trial 2334
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=132, random_state=100,
subsample=0.75))])
cv score: [0.5825 0.6225 0.68583333 0.7575 0.75079114]
----------------------------------------
Trial 2335
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=79, random_state=100,
subsample=0.6))])
cv score: [0.48458333 0.57 0.51541667 0.56666667 0.53718354]
----------------------------------------
Trial 2336
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=43, random_state=100,
subsample=0.9))])
cv score: [0.63916667 0.6225 0.67 0.8 0.8528481 ]
----------------------------------------
Trial 2337
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=122, random_state=100,
subsample=0.7))])
cv score: [0.69 0.60416667 0.72666667 0.78833333 0.78955696]
----------------------------------------
Trial 2338
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=174,
random_state=100))])
cv score: [0.66166667 0.64083333 0.7375 0.79916667 0.85047468]
----------------------------------------
Trial 2339
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
n_estimators=143, random_state=100,
subsample=0.6))])
cv score: [0.73166667 0.66583333 0.755 0.83416667 0.83781646]
----------------------------------------
Trial 2340
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='log2',
n_estimators=75, random_state=100,
subsample=0.95))])
cv score: [0.61166667 0.64666667 0.70041667 0.81041667 0.79509494]
----------------------------------------
Trial 2341
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=13,
random_state=100))])
cv score: [0.68708333 0.48916667 0.77833333 0.6725 0.64161392]
----------------------------------------
Trial 2342
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=106,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73 0.70583333 0.78416667 0.84666667 0.8971519 ]
----------------------------------------
Trial 2343
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76083333 0.75333333 0.795 0.83875 0.88132911]
----------------------------------------
Trial 2344
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=43,
random_state=100))])
cv score: [0.59375 0.68541667 0.70458333 0.82791667 0.83306962]
----------------------------------------
Trial 2345
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=29,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.7175 0.78708333 0.83583333 0.89952532]
----------------------------------------
Trial 2346
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=183,
random_state=100))])
cv score: [0.54458333 0.66208333 0.71458333 0.82333333 0.7903481 ]
----------------------------------------
Trial 2347
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='sqrt',
n_estimators=37, random_state=100,
subsample=0.95))])
cv score: [0.61083333 0.64083333 0.72125 0.79458333 0.81170886]
----------------------------------------
Trial 2348
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='sqrt',
n_estimators=148, random_state=100,
subsample=0.65))])
cv score: [0.6975 0.70416667 0.67083333 0.77666667 0.81091772]
----------------------------------------
Trial 2349
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.675 0.69 0.745 0.80333333 0.8789557 ]
----------------------------------------
Trial 2350
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66375 0.73875 0.76583333 0.855 0.86946203]
----------------------------------------
Trial 2351
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=37, random_state=100))])
cv score: [0.63291667 0.64375 0.75541667 0.83666667 0.78995253]
----------------------------------------
Trial 2352
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=24, random_state=100))])
cv score: [0.67375 0.64708333 0.70625 0.77625 0.81210443]
----------------------------------------
Trial 2353
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=133,
random_state=100, subsample=0.8))])
cv score: [0.67916667 0.65833333 0.74583333 0.81 0.82832278]
----------------------------------------
Trial 2354
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='log2',
n_estimators=42, random_state=100,
subsample=0.6))])
cv score: [0.69333333 0.69416667 0.66833333 0.80416667 0.81091772]
----------------------------------------
Trial 2355
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=24, random_state=100,
subsample=0.7))])
cv score: [0.63666667 0.63083333 0.72041667 0.7725 0.76977848]
----------------------------------------
Trial 2356
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=92, random_state=100))])
cv score: [0.70625 0.62958333 0.73 0.81625 0.80181962]
----------------------------------------
Trial 2357
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.73708333 0.78083333 0.865 0.84651899]
----------------------------------------
Trial 2358
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=168, random_state=100))])
cv score: [0.67166667 0.6225 0.72666667 0.8 0.8346519 ]
----------------------------------------
Trial 2359
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=57,
random_state=100))])
cv score: [0.61416667 0.6025 0.73708333 0.74166667 0.81289557]
----------------------------------------
Trial 2360
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=162, random_state=100,
subsample=0.8))])
cv score: [0.7475 0.6675 0.78583333 0.8225 0.82674051]
----------------------------------------
Trial 2361
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=172, random_state=100,
subsample=0.6))])
cv score: [0.6725 0.64416667 0.74 0.8175 0.76977848]
----------------------------------------
Trial 2362
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=32,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66583333 0.62416667 0.80083333 0.6275 0.75474684]
----------------------------------------
Trial 2363
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=14, random_state=100,
subsample=0.6))])
cv score: [0.5875 0.6225 0.59666667 0.5825 0.70411392]
----------------------------------------
Trial 2364
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=10, random_state=100))])
cv score: [0.66916667 0.6375 0.72208333 0.81708333 0.83386076]
----------------------------------------
Trial 2365
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=32,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.73333333 0.8 0.85583333 0.86946203]
----------------------------------------
Trial 2366
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='log2',
n_estimators=86, random_state=100,
subsample=0.9))])
cv score: [0.745 0.59083333 0.69333333 0.79583333 0.8125 ]
----------------------------------------
Trial 2367
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66166667 0.66416667 0.77916667 0.79833333 0.86155063]
----------------------------------------
Trial 2368
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6125 0.75083333 0.77208333 0.81791667 0.88093354]
----------------------------------------
Trial 2369
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=163, random_state=100))])
cv score: [0.60416667 0.69083333 0.7025 0.80833333 0.83386076]
----------------------------------------
Trial 2370
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='sqrt',
n_estimators=162, random_state=100,
subsample=0.7))])
cv score: [0.635 0.59083333 0.64666667 0.69833333 0.66297468]
----------------------------------------
Trial 2371
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=64, random_state=100))])
cv score: [0.6475 0.62666667 0.75 0.81 0.83386076]
----------------------------------------
Trial 2372
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=150,
random_state=100,
subsample=0.85))])
cv score: [0.6825 0.65333333 0.75333333 0.805 0.81566456]
----------------------------------------
Trial 2373
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=173,
random_state=100))])
cv score: [0.68708333 0.49375 0.80833333 0.64625 0.75276899]
----------------------------------------
Trial 2374
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6375 0.64916667 0.77916667 0.7625 0.83227848]
----------------------------------------
Trial 2375
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=23,
random_state=100))])
cv score: [0.645 0.63166667 0.7325 0.7975 0.87816456]
----------------------------------------
Trial 2376
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='sqrt',
n_estimators=95, random_state=100,
subsample=0.6))])
cv score: [0.59666667 0.7225 0.69333333 0.77583333 0.84572785]
----------------------------------------
Trial 2377
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=199, random_state=100,
subsample=0.65))])
cv score: [0.66083333 0.58583333 0.70666667 0.78 0.78876582]
----------------------------------------
Trial 2378
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='sqrt',
n_estimators=62,
random_state=100))])
cv score: [0.695 0.64083333 0.75083333 0.78083333 0.85917722]
----------------------------------------
Trial 2379
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=138,
random_state=100))])
cv score: [0.70083333 0.60416667 0.6825 0.805 0.83860759]
----------------------------------------
Trial 2380
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='sqrt',
n_estimators=106, random_state=100,
subsample=0.8))])
cv score: [0.68083333 0.66916667 0.73 0.80083333 0.77294304]
----------------------------------------
Trial 2381
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
n_estimators=136, random_state=100,
subsample=0.7))])
cv score: [0.77333333 0.66541667 0.75666667 0.86083333 0.88686709]
----------------------------------------
Trial 2382
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=78,
random_state=100))])
cv score: [0.68583333 0.5925 0.67416667 0.81166667 0.84098101]
----------------------------------------
Trial 2383
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=88,
random_state=100))])
cv score: [0.67583333 0.59 0.68 0.81416667 0.83544304]
----------------------------------------
Trial 2384
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=166, random_state=100,
subsample=0.85))])
cv score: [0.60666667 0.56666667 0.69666667 0.775 0.68275316]
----------------------------------------
Trial 2385
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=115, random_state=100))])
cv score: [0.61583333 0.70166667 0.7025 0.79583333 0.82753165]
----------------------------------------
Trial 2386
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=191,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.74541667 0.78291667 0.86 0.85363924]
----------------------------------------
Trial 2387
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76333333 0.72416667 0.79166667 0.8375 0.92721519]
----------------------------------------
Trial 2388
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=51,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.725 0.79666667 0.82666667 0.8971519 ]
----------------------------------------
Trial 2389
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=69, random_state=100))])
cv score: [0.65 0.65333333 0.705 0.82666667 0.8528481 ]
----------------------------------------
Trial 2390
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
n_estimators=91, random_state=100,
subsample=0.6))])
cv score: [0.6425 0.6875 0.70333333 0.82333333 0.73417722]
----------------------------------------
Trial 2391
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=166, random_state=100))])
cv score: [0.57583333 0.70083333 0.705 0.81666667 0.81566456]
----------------------------------------
Trial 2392
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=129, random_state=100))])
cv score: [0.76583333 0.68416667 0.75333333 0.85333333 0.88924051]
----------------------------------------
Trial 2393
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=176,
random_state=100))])
cv score: [0.73041667 0.64916667 0.74916667 0.7725 0.73259494]
----------------------------------------
Trial 2394
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=22, random_state=100))])
cv score: [0.73416667 0.665 0.77541667 0.84125 0.83188291]
----------------------------------------
Trial 2395
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77333333 0.735 0.79 0.84583333 0.87974684]
----------------------------------------
Trial 2396
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=82,
random_state=100))])
cv score: [0.7 0.63875 0.7075 0.79083333 0.83860759]
----------------------------------------
Trial 2397
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=15,
random_state=100))])
cv score: [0.67958333 0.51375 0.83208333 0.665 0.66890823]
----------------------------------------
Trial 2398
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=49,
random_state=100))])
cv score: [0.62833333 0.66625 0.68666667 0.80791667 0.83939873]
----------------------------------------
Trial 2399
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=115,
random_state=100))])
cv score: [0.56791667 0.65458333 0.69166667 0.83208333 0.80181962]
----------------------------------------
Trial 2400
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=48,
random_state=100))])
cv score: [0.63416667 0.66458333 0.68583333 0.80375 0.84098101]
----------------------------------------
Trial 2401
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=73, random_state=100))])
cv score: [0.66208333 0.63291667 0.73916667 0.82791667 0.79311709]
----------------------------------------
Trial 2402
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=67,
random_state=100))])
cv score: [0.5625 0.66416667 0.6825 0.82708333 0.83623418]
----------------------------------------
Trial 2403
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=112, random_state=100))])
cv score: [0.61333333 0.6975 0.70333333 0.79833333 0.8306962 ]
----------------------------------------
Trial 2404
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=33, random_state=100,
subsample=0.6))])
cv score: [0.34708333 0.48333333 0.62333333 0.665 0.52492089]
----------------------------------------
Trial 2405
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=152, random_state=100,
subsample=0.75))])
cv score: [0.68166667 0.59916667 0.72 0.81416667 0.80775316]
----------------------------------------
Trial 2406
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62083333 0.63083333 0.68 0.68333333 0.74208861]
----------------------------------------
Trial 2407
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=117, random_state=100))])
cv score: [0.5625 0.7175 0.7025 0.78833333 0.79667722]
----------------------------------------
Trial 2408
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=151,
random_state=100))])
cv score: [0.695 0.60333333 0.67833333 0.8125 0.84335443]
----------------------------------------
Trial 2409
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=186, random_state=100,
subsample=0.7))])
cv score: [0.65916667 0.59833333 0.69 0.7375 0.82674051]
----------------------------------------
Trial 2410
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=166,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6025 0.68166667 0.80166667 0.72583333 0.80775316]
----------------------------------------
Trial 2411
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.85))])
cv score: [0.66 0.6325 0.66416667 0.81166667 0.80221519]
----------------------------------------
Trial 2412
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=23, random_state=100))])
cv score: [0.74916667 0.64041667 0.6875 0.70541667 0.875 ]
----------------------------------------
Trial 2413
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=168, random_state=100))])
cv score: [0.7775 0.69583333 0.77333333 0.85333333 0.8710443 ]
----------------------------------------
Trial 2414
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
n_estimators=44, random_state=100,
subsample=0.7))])
cv score: [0.70666667 0.69666667 0.81666667 0.83833333 0.76977848]
----------------------------------------
Trial 2415
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=198, random_state=100,
subsample=0.7))])
cv score: [0.6525 0.63416667 0.67166667 0.80833333 0.82911392]
----------------------------------------
Trial 2416
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=174,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65833333 0.64916667 0.76583333 0.7725 0.83939873]
----------------------------------------
Trial 2417
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=16,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5675 0.72958333 0.735 0.82583333 0.83306962]
----------------------------------------
Trial 2418
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=155,
random_state=100))])
cv score: [0.68583333 0.51375 0.82291667 0.66458333 0.66416139]
----------------------------------------
Trial 2419
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=105, random_state=100,
subsample=0.6))])
cv score: [0.46333333 0.655 0.635 0.66416667 0.66139241]
----------------------------------------
Trial 2420
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.69416667 0.76833333 0.78583333 0.84414557]
----------------------------------------
Trial 2421
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7475 0.72916667 0.79083333 0.80583333 0.85601266]
----------------------------------------
Trial 2422
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=34,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74833333 0.75666667 0.78791667 0.84333333 0.86392405]
----------------------------------------
Trial 2423
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=31,
random_state=100))])
cv score: [0.68166667 0.68625 0.68333333 0.80291667 0.85443038]
----------------------------------------
Trial 2424
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.6))])
cv score: [0.67333333 0.66583333 0.71166667 0.8125 0.81170886]
----------------------------------------
Trial 2425
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=156, random_state=100,
subsample=0.9))])
cv score: [0.5675 0.57333333 0.62166667 0.62583333 0.78401899]
----------------------------------------
Trial 2426
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=153, random_state=100,
subsample=0.95))])
cv score: [0.58583333 0.65 0.7625 0.71 0.68908228]
----------------------------------------
Trial 2427
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=155,
random_state=100))])
cv score: [0.69 0.63 0.69833333 0.7975 0.82674051]
----------------------------------------
Trial 2428
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=18,
random_state=100))])
cv score: [0.59083333 0.50958333 0.69333333 0.76166667 0.81170886]
----------------------------------------
Trial 2429
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
n_estimators=64, random_state=100,
subsample=0.7))])
cv score: [0.72916667 0.66583333 0.79166667 0.8425 0.8306962 ]
----------------------------------------
Trial 2430
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=34,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77583333 0.7275 0.76875 0.85583333 0.92721519]
----------------------------------------
Trial 2431
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=74,
random_state=100))])
cv score: [0.69375 0.51791667 0.79958333 0.66958333 0.62974684]
----------------------------------------
Trial 2432
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=174,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.71833333 0.78083333 0.82166667 0.90506329]
----------------------------------------
Trial 2433
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=111,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75833333 0.70166667 0.79833333 0.83666667 0.90901899]
----------------------------------------
Trial 2434
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='log2',
n_estimators=172, random_state=100,
subsample=0.95))])
cv score: [0.6925 0.57833333 0.725 0.78583333 0.82199367]
----------------------------------------
Trial 2435
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=48,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73833333 0.72583333 0.7775 0.815 0.90822785]
----------------------------------------
Trial 2436
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=97, random_state=100))])
cv score: [0.70583333 0.625 0.72166667 0.79166667 0.83227848]
----------------------------------------
Trial 2437
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=32, random_state=100,
subsample=0.8))])
cv score: [0.7 0.58583333 0.71083333 0.8375 0.82436709]
----------------------------------------
Trial 2438
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=190,
random_state=100))])
cv score: [0.69416667 0.51791667 0.7975 0.66958333 0.62816456]
----------------------------------------
Trial 2439
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=197,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57041667 0.64291667 0.65833333 0.8 0.76344937]
----------------------------------------
Trial 2440
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=186,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6125 0.63833333 0.74916667 0.67 0.74446203]
----------------------------------------
Trial 2441
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='log2',
n_estimators=152, random_state=100,
subsample=0.6))])
cv score: [0.62916667 0.56 0.64166667 0.71 0.74683544]
----------------------------------------
Trial 2442
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2',
random_state=100))])
cv score: [0.56375 0.66125 0.69375 0.83208333 0.79628165]
----------------------------------------
Trial 2443
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57833333 0.56833333 0.685 0.70416667 0.7943038 ]
----------------------------------------
Trial 2444
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72083333 0.715 0.73208333 0.86083333 0.86629747]
----------------------------------------
Trial 2445
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='log2',
n_estimators=36, random_state=100,
subsample=0.7))])
cv score: [0.66916667 0.72666667 0.70083333 0.8325 0.86629747]
----------------------------------------
Trial 2446
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='sqrt',
n_estimators=178, random_state=100,
subsample=0.8))])
cv score: [0.67833333 0.59083333 0.67 0.74833333 0.7681962 ]
----------------------------------------
Trial 2447
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=160, random_state=100))])
cv score: [0.67583333 0.6575 0.71916667 0.805 0.85601266]
----------------------------------------
Trial 2448
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=39, random_state=100,
subsample=0.8))])
cv score: [0.67166667 0.62416667 0.7575 0.75083333 0.67563291]
----------------------------------------
Trial 2449
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65833333 0.64916667 0.76666667 0.7475 0.81170886]
----------------------------------------
Trial 2450
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='sqrt',
n_estimators=176, random_state=100,
subsample=0.9))])
cv score: [0.65416667 0.63666667 0.73416667 0.8425 0.81329114]
----------------------------------------
Trial 2451
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=183, random_state=100))])
cv score: [0.70666667 0.63083333 0.72166667 0.79833333 0.8306962 ]
----------------------------------------
Trial 2452
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=89, random_state=100))])
cv score: [0.61333333 0.7 0.70416667 0.78916667 0.83544304]
----------------------------------------
Trial 2453
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
n_estimators=120,
random_state=100))])
cv score: [0.75583333 0.63583333 0.74583333 0.87 0.77452532]
----------------------------------------
Trial 2454
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=77, random_state=100,
subsample=0.65))])
cv score: [0.68083333 0.5 0.48666667 0.64083333 0.75316456]
----------------------------------------
Trial 2455
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=183,
random_state=100))])
cv score: [0.54458333 0.66208333 0.71458333 0.82333333 0.7903481 ]
----------------------------------------
Trial 2456
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79166667 0.74625 0.7675 0.855 0.91297468]
----------------------------------------
Trial 2457
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='sqrt',
n_estimators=142, random_state=100,
subsample=0.85))])
cv score: [0.68166667 0.5875 0.70833333 0.76166667 0.76740506]
----------------------------------------
Trial 2458
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=141, random_state=100))])
cv score: [0.6975 0.63916667 0.73166667 0.81166667 0.83623418]
----------------------------------------
Trial 2459
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=25, random_state=100,
subsample=0.75))])
cv score: [0.53 0.61375 0.675 0.82375 0.7721519]
----------------------------------------
Trial 2460
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='sqrt',
n_estimators=36, random_state=100,
subsample=0.7))])
cv score: [0.68083333 0.58833333 0.7425 0.79083333 0.82753165]
----------------------------------------
Trial 2461
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=150,
random_state=100))])
cv score: [0.6925 0.4875 0.71875 0.66916667 0.63568038]
----------------------------------------
Trial 2462
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='sqrt',
n_estimators=53, random_state=100,
subsample=0.75))])
cv score: [0.60416667 0.5825 0.68083333 0.80833333 0.75079114]
----------------------------------------
Trial 2463
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=111,
random_state=100))])
cv score: [0.61166667 0.67666667 0.7 0.81083333 0.82753165]
----------------------------------------
Trial 2464
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72416667 0.6275 0.735 0.7425 0.83702532]
----------------------------------------
Trial 2465
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=175,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.6575 0.78083333 0.8 0.89082278]
----------------------------------------
Trial 2466
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=15, random_state=100,
subsample=0.65))])
cv score: [0.58416667 0.64583333 0.705 0.745 0.63132911]
----------------------------------------
Trial 2467
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69833333 0.76958333 0.77583333 0.81375 0.87816456]
----------------------------------------
Trial 2468
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=90, random_state=100))])
cv score: [0.56916667 0.73333333 0.69416667 0.79333333 0.80537975]
----------------------------------------
Trial 2469
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=96, random_state=100))])
cv score: [0.695 0.61416667 0.7275 0.82291667 0.81724684]
----------------------------------------
Trial 2470
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71916667 0.71666667 0.76625 0.86083333 0.86629747]
----------------------------------------
Trial 2471
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
n_estimators=58, random_state=100,
subsample=0.6))])
cv score: [0.76166667 0.71833333 0.7425 0.85833333 0.82674051]
----------------------------------------
Trial 2472
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7275 0.6775 0.7975 0.84166667 0.91297468]
----------------------------------------
Trial 2473
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=85,
random_state=100))])
cv score: [0.70083333 0.64125 0.71 0.8 0.83623418]
----------------------------------------
Trial 2474
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=184,
random_state=100))])
cv score: [0.6375 0.56583333 0.73125 0.78791667 0.79588608]
----------------------------------------
Trial 2475
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=36, random_state=100))])
cv score: [0.74666667 0.68291667 0.77666667 0.84583333 0.85443038]
----------------------------------------
Trial 2476
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=24, random_state=100))])
cv score: [0.64458333 0.59583333 0.73416667 0.78166667 0.78164557]
----------------------------------------
Trial 2477
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=111,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58166667 0.645 0.77333333 0.70166667 0.77689873]
----------------------------------------
Trial 2478
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.665 0.65416667 0.785 0.7725 0.82594937]
----------------------------------------
Trial 2479
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=128, random_state=100,
subsample=0.8))])
cv score: [0.76416667 0.67916667 0.765 0.855 0.87262658]
----------------------------------------
Trial 2480
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
n_estimators=49, random_state=100,
subsample=0.7))])
cv score: [0.765 0.665 0.72833333 0.77083333 0.78481013]
----------------------------------------
Trial 2481
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=101, random_state=100,
subsample=0.75))])
cv score: [0.555 0.6275 0.59583333 0.6275 0.65110759]
----------------------------------------
Trial 2482
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64833333 0.68166667 0.77333333 0.765 0.88291139]
----------------------------------------
Trial 2483
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.85))])
cv score: [0.69833333 0.585 0.74 0.79416667 0.7903481 ]
----------------------------------------
Trial 2484
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
n_estimators=17, random_state=100,
subsample=0.7))])
cv score: [0.66416667 0.69833333 0.76916667 0.82833333 0.82120253]
----------------------------------------
Trial 2485
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=134, random_state=100,
subsample=0.75))])
cv score: [0.7175 0.62583333 0.705 0.775 0.79825949]
----------------------------------------
Trial 2486
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='sqrt',
n_estimators=181,
random_state=100))])
cv score: [0.6625 0.63166667 0.715 0.76166667 0.78401899]
----------------------------------------
Trial 2487
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63333333 0.64333333 0.78083333 0.75583333 0.82990506]
----------------------------------------
Trial 2488
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=49,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5825 0.64583333 0.78166667 0.7275 0.71123418]
----------------------------------------
Trial 2489
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=183, random_state=100,
subsample=0.65))])
cv score: [0.51541667 0.62333333 0.71916667 0.6275 0.2943038 ]
----------------------------------------
Trial 2490
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=32, random_state=100))])
cv score: [0.655 0.65333333 0.725 0.79833333 0.83148734]
----------------------------------------
Trial 2491
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=136, random_state=100))])
cv score: [0.725 0.68541667 0.78333333 0.83416667 0.83386076]
----------------------------------------
Trial 2492
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=81, random_state=100,
subsample=0.75))])
cv score: [0.655 0.66416667 0.71083333 0.84 0.85047468]
----------------------------------------
Trial 2493
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='log2',
n_estimators=103, random_state=100,
subsample=0.8))])
cv score: [0.59666667 0.54 0.65 0.65666667 0.73101266]
----------------------------------------
Trial 2494
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='log2',
n_estimators=144,
random_state=100))])
cv score: [0.675 0.595 0.6975 0.74666667 0.79113924]
----------------------------------------
Trial 2495
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=176,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58833333 0.61333333 0.73833333 0.74583333 0.83227848]
----------------------------------------
Trial 2496
----------------------------------------
Parameters {'xgb__n_estimators': 160, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=160,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.54875 0.645 0.71666667 0.6575 0.75158228]
----------------------------------------
Trial 2497
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=36,
random_state=100))])
cv score: [0.73666667 0.62 0.74833333 0.83166667 0.83781646]
----------------------------------------
Trial 2498
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65166667 0.60333333 0.72166667 0.74166667 0.80696203]
----------------------------------------
Trial 2499
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=146, random_state=100,
subsample=0.75))])
cv score: [0.66 0.695 0.69166667 0.73666667 0.74367089]
----------------------------------------
Trial 2500
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74666667 0.68416667 0.76416667 0.78916667 0.88607595]
----------------------------------------
Trial 2501
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=159, random_state=100,
subsample=0.95))])
cv score: [0.66166667 0.58666667 0.68416667 0.81916667 0.82911392]
----------------------------------------
Trial 2502
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=184,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.68666667 0.77666667 0.79416667 0.87420886]
----------------------------------------
Trial 2503
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=169,
random_state=100))])
cv score: [0.76458333 0.49375 0.78708333 0.7025 0.79232595]
----------------------------------------
Trial 2504
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=158,
random_state=100))])
cv score: [0.54375 0.66208333 0.70875 0.82416667 0.78085443]
----------------------------------------
Trial 2505
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
n_estimators=76, random_state=100,
subsample=0.65))])
cv score: [0.72666667 0.69583333 0.78916667 0.82416667 0.83544304]
----------------------------------------
Trial 2506
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63666667 0.63833333 0.73416667 0.75916667 0.79905063]
----------------------------------------
Trial 2507
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, n_estimators=91,
random_state=100,
subsample=0.65))])
cv score: [0.6625 0.68833333 0.71416667 0.77166667 0.73259494]
----------------------------------------
Trial 2508
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=90,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7575 0.74 0.77208333 0.84 0.87262658]
----------------------------------------
Trial 2509
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=29, random_state=100))])
cv score: [0.72291667 0.66875 0.78833333 0.84625 0.84454114]
----------------------------------------
Trial 2510
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72083333 0.72666667 0.77208333 0.85833333 0.8568038 ]
----------------------------------------
Trial 2511
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=52, random_state=100))])
cv score: [0.62875 0.64125 0.73625 0.83791667 0.78639241]
----------------------------------------
Trial 2512
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=79,
random_state=100))])
cv score: [0.70833333 0.63958333 0.70833333 0.79666667 0.83623418]
----------------------------------------
Trial 2513
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=103,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7975 0.72291667 0.79708333 0.84333333 0.8528481 ]
----------------------------------------
Trial 2514
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=195,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5825 0.59666667 0.74916667 0.72125 0.75474684]
----------------------------------------
Trial 2515
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=183, random_state=100))])
cv score: [0.7675 0.68791667 0.77833333 0.84 0.85363924]
----------------------------------------
Trial 2516
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='log2',
n_estimators=73, random_state=100,
subsample=0.75))])
cv score: [0.57458333 0.67 0.69041667 0.79833333 0.79588608]
----------------------------------------
Trial 2517
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=113,
random_state=100))])
cv score: [0.69083333 0.63583333 0.69916667 0.80416667 0.82674051]
----------------------------------------
Trial 2518
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=91, random_state=100))])
cv score: [0.71916667 0.64166667 0.73916667 0.78166667 0.82990506]
----------------------------------------
Trial 2519
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7225 0.73416667 0.75666667 0.84833333 0.86471519]
----------------------------------------
Trial 2520
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=196,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 2521
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=92,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.70666667 0.79333333 0.79166667 0.88686709]
----------------------------------------
Trial 2522
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=183, random_state=100))])
cv score: [0.77666667 0.6925 0.77833333 0.85416667 0.8710443 ]
----------------------------------------
Trial 2523
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=73,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76833333 0.7125 0.80333333 0.8425 0.87579114]
----------------------------------------
Trial 2524
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=102, random_state=100,
subsample=0.85))])
cv score: [0.64666667 0.65416667 0.69166667 0.79333333 0.82199367]
----------------------------------------
Trial 2525
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=70,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.70583333 0.75375 0.86166667 0.8528481 ]
----------------------------------------
Trial 2526
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=13, random_state=100))])
cv score: [0.67791667 0.61416667 0.66958333 0.70333333 0.72151899]
----------------------------------------
Trial 2527
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=128, random_state=100,
subsample=0.7))])
cv score: [0.54833333 0.70916667 0.68416667 0.74166667 0.69462025]
----------------------------------------
Trial 2528
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=126,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71583333 0.68666667 0.785 0.81 0.82911392]
----------------------------------------
Trial 2529
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=72, random_state=100))])
cv score: [0.69083333 0.61666667 0.7275 0.7825 0.82911392]
----------------------------------------
Trial 2530
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='log2',
n_estimators=14, random_state=100,
subsample=0.7))])
cv score: [0.6425 0.55 0.66833333 0.7675 0.74208861]
----------------------------------------
Trial 2531
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=107, random_state=100))])
cv score: [0.73583333 0.69125 0.725 0.86 0.87341772]
----------------------------------------
Trial 2532
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=94, random_state=100))])
cv score: [0.6525 0.62583333 0.74916667 0.81416667 0.83306962]
----------------------------------------
Trial 2533
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=190,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64833333 0.655 0.7775 0.7925 0.86946203]
----------------------------------------
Trial 2534
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72666667 0.67666667 0.775 0.76083333 0.84572785]
----------------------------------------
Trial 2535
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63166667 0.73333333 0.72583333 0.86166667 0.80577532]
----------------------------------------
Trial 2536
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76916667 0.73166667 0.775 0.8425 0.92167722]
----------------------------------------
Trial 2537
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6475 0.615 0.79333333 0.76333333 0.81487342]
----------------------------------------
Trial 2538
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=129,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56166667 0.625 0.78083333 0.73416667 0.82753165]
----------------------------------------
Trial 2539
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=185, random_state=100))])
cv score: [0.685 0.63416667 0.71916667 0.79833333 0.83939873]
----------------------------------------
Trial 2540
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=39,
random_state=100))])
cv score: [0.64833333 0.65458333 0.69 0.81541667 0.83227848]
----------------------------------------
Trial 2541
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=183,
random_state=100))])
cv score: [0.68083333 0.58583333 0.70416667 0.80333333 0.81012658]
----------------------------------------
Trial 2542
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='log2',
n_estimators=103, random_state=100,
subsample=0.6))])
cv score: [0.59416667 0.6725 0.67208333 0.75 0.75949367]
----------------------------------------
Trial 2543
----------------------------------------
Parameters {'xgb__n_estimators': 115, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=115,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72541667 0.73 0.76083333 0.85916667 0.89873418]
----------------------------------------
Trial 2544
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='log2',
n_estimators=167, random_state=100,
subsample=0.8))])
cv score: [0.65416667 0.64416667 0.67166667 0.83 0.84889241]
----------------------------------------
Trial 2545
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7825 0.69875 0.77708333 0.83333333 0.90031646]
----------------------------------------
Trial 2546
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=21,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 2547
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='log2',
random_state=100, subsample=0.7))])
cv score: [0.62833333 0.70583333 0.68333333 0.79583333 0.79825949]
----------------------------------------
Trial 2548
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=90,
random_state=100))])
cv score: [0.64333333 0.66916667 0.70416667 0.8075 0.84177215]
----------------------------------------
Trial 2549
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=158, random_state=100,
subsample=0.8))])
cv score: [0.68333333 0.66166667 0.70583333 0.82416667 0.82357595]
----------------------------------------
Trial 2550
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=67, random_state=100,
subsample=0.65))])
cv score: [0.435 0.42916667 0.56 0.59958333 0.62737342]
----------------------------------------
Trial 2551
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57208333 0.64875 0.67041667 0.81458333 0.78718354]
----------------------------------------
Trial 2552
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=49, random_state=100))])
cv score: [0.72291667 0.66416667 0.78083333 0.82625 0.84335443]
----------------------------------------
Trial 2553
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=74,
random_state=100))])
cv score: [0.73416667 0.59583333 0.77583333 0.7475 0.76977848]
----------------------------------------
Trial 2554
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=87,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70541667 0.735 0.75875 0.86208333 0.87618671]
----------------------------------------
Trial 2555
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75666667 0.70833333 0.7875 0.80583333 0.85126582]
----------------------------------------
Trial 2556
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=35, random_state=100))])
cv score: [0.725 0.66666667 0.78083333 0.845 0.85363924]
----------------------------------------
Trial 2557
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=102, random_state=100,
subsample=0.9))])
cv score: [0.6075 0.53833333 0.74 0.79083333 0.79984177]
----------------------------------------
Trial 2558
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63916667 0.65083333 0.75916667 0.78083333 0.82436709]
----------------------------------------
Trial 2559
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=132, random_state=100))])
cv score: [0.74083333 0.68291667 0.79166667 0.83166667 0.83860759]
----------------------------------------
Trial 2560
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=161,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73958333 0.73666667 0.76333333 0.86666667 0.83702532]
----------------------------------------
Trial 2561
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=10,
random_state=100))])
cv score: [0.57791667 0.67875 0.66791667 0.82333333 0.84256329]
----------------------------------------
Trial 2562
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=151,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59333333 0.6225 0.72916667 0.71583333 0.76898734]
----------------------------------------
Trial 2563
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=37, random_state=100))])
cv score: [0.60666667 0.72375 0.69208333 0.82416667 0.87935127]
----------------------------------------
Trial 2564
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=130, random_state=100,
subsample=0.9))])
cv score: [0.74583333 0.61625 0.79 0.83166667 0.78797468]
----------------------------------------
Trial 2565
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=92, random_state=100))])
cv score: [0.78083333 0.68166667 0.755 0.84583333 0.87816456]
----------------------------------------
Trial 2566
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=55, random_state=100))])
cv score: [0.65958333 0.60375 0.705 0.79666667 0.80973101]
----------------------------------------
Trial 2567
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='sqrt',
n_estimators=70, random_state=100,
subsample=0.95))])
cv score: [0.68416667 0.59833333 0.73416667 0.74916667 0.78797468]
----------------------------------------
Trial 2568
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6625 0.68 0.75583333 0.7825 0.88053797]
----------------------------------------
Trial 2569
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=187, random_state=100))])
cv score: [0.68916667 0.63666667 0.71916667 0.8075 0.84256329]
----------------------------------------
Trial 2570
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=83, random_state=100))])
cv score: [0.76 0.6925 0.74416667 0.85666667 0.88370253]
----------------------------------------
Trial 2571
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.8))])
cv score: [0.69416667 0.61666667 0.73333333 0.80583333 0.82990506]
----------------------------------------
Trial 2572
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=89, random_state=100,
subsample=0.65))])
cv score: [0.7 0.62333333 0.72 0.81583333 0.8125 ]
----------------------------------------
Trial 2573
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64166667 0.66583333 0.78166667 0.73083333 0.80063291]
----------------------------------------
Trial 2574
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=62,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.645 0.63416667 0.79083333 0.72083333 0.75553797]
----------------------------------------
Trial 2575
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62083333 0.6 0.77333333 0.74583333 0.84889241]
----------------------------------------
Trial 2576
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.72083333 0.77375 0.8575 0.87262658]
----------------------------------------
Trial 2577
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=184, random_state=100,
subsample=0.75))])
cv score: [0.65416667 0.66166667 0.685 0.7475 0.70015823]
----------------------------------------
Trial 2578
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75833333 0.70916667 0.80916667 0.845 0.88291139]
----------------------------------------
Trial 2579
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=104, random_state=100,
subsample=0.75))])
cv score: [0.67 0.60083333 0.7075 0.8 0.79509494]
----------------------------------------
Trial 2580
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=36,
random_state=100))])
cv score: [0.75583333 0.58791667 0.81083333 0.78541667 0.80379747]
----------------------------------------
Trial 2581
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=128, random_state=100))])
cv score: [0.68916667 0.5975 0.74166667 0.82125 0.82515823]
----------------------------------------
Trial 2582
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=146, random_state=100))])
cv score: [0.76 0.68083333 0.78416667 0.83583333 0.85205696]
----------------------------------------
Trial 2583
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=141, random_state=100,
subsample=0.75))])
cv score: [0.5675 0.6425 0.675 0.78583333 0.77768987]
----------------------------------------
Trial 2584
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
n_estimators=73, random_state=100,
subsample=0.75))])
cv score: [0.76083333 0.64333333 0.80416667 0.84166667 0.84731013]
----------------------------------------
Trial 2585
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=147,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60166667 0.63166667 0.71583333 0.67583333 0.8346519 ]
----------------------------------------
Trial 2586
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=97, random_state=100,
subsample=0.9))])
cv score: [0.53208333 0.64333333 0.68125 0.79791667 0.76147152]
----------------------------------------
Trial 2587
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=21,
random_state=100))])
cv score: [0.68916667 0.50125 0.725 0.64958333 0.63568038]
----------------------------------------
Trial 2588
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=163, random_state=100))])
cv score: [0.71583333 0.67625 0.78166667 0.84 0.84256329]
----------------------------------------
Trial 2589
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73666667 0.685 0.80083333 0.81916667 0.8789557 ]
----------------------------------------
Trial 2590
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62916667 0.68416667 0.71208333 0.7125 0.78639241]
----------------------------------------
Trial 2591
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=111, random_state=100))])
cv score: [0.70583333 0.60083333 0.73666667 0.83083333 0.82001582]
----------------------------------------
Trial 2592
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=149,
random_state=100))])
cv score: [0.69166667 0.60083333 0.6825 0.805 0.84256329]
----------------------------------------
Trial 2593
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.68166667 0.78833333 0.79166667 0.83939873]
----------------------------------------
Trial 2594
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
n_estimators=49, random_state=100,
subsample=0.65))])
cv score: [0.73833333 0.675 0.695 0.73083333 0.72151899]
----------------------------------------
Trial 2595
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=17, random_state=100))])
cv score: [0.73541667 0.64958333 0.78125 0.85 0.82634494]
----------------------------------------
Trial 2596
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=106,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 2597
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=128, random_state=100))])
cv score: [0.71416667 0.68333333 0.78666667 0.83166667 0.83623418]
----------------------------------------
Trial 2598
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
n_estimators=193, random_state=100,
subsample=0.8))])
cv score: [0.59666667 0.65583333 0.74166667 0.775 0.75791139]
----------------------------------------
Trial 2599
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='log2',
n_estimators=52, random_state=100,
subsample=0.75))])
cv score: [0.69083333 0.67583333 0.68416667 0.82333333 0.78876582]
----------------------------------------
Trial 2600
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68 0.68 0.78083333 0.77833333 0.83306962]
----------------------------------------
Trial 2601
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=107,
random_state=100))])
cv score: [0.6475 0.67166667 0.70333333 0.805 0.84256329]
----------------------------------------
Trial 2602
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=122,
random_state=100))])
cv score: [0.56375 0.65958333 0.68833333 0.83458333 0.7994462 ]
----------------------------------------
Trial 2603
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=19, random_state=100,
subsample=0.95))])
cv score: [0.65833333 0.62291667 0.72375 0.80666667 0.7539557 ]
----------------------------------------
Trial 2604
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=195, random_state=100))])
cv score: [0.75666667 0.67833333 0.78333333 0.84 0.84731013]
----------------------------------------
Trial 2605
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=68,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.50083333 0.73541667 0.71041667 0.79833333 0.73496835]
----------------------------------------
Trial 2606
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=38, random_state=100))])
cv score: [0.68666667 0.68791667 0.71666667 0.75708333 0.875 ]
----------------------------------------
Trial 2607
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=48,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 2608
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=43, random_state=100))])
cv score: [0.66291667 0.62666667 0.72708333 0.82833333 0.78085443]
----------------------------------------
Trial 2609
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=104,
random_state=100))])
cv score: [0.69333333 0.50125 0.72541667 0.64916667 0.63330696]
----------------------------------------
Trial 2610
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=166, random_state=100))])
cv score: [0.675 0.61666667 0.73083333 0.79916667 0.83386076]
----------------------------------------
Trial 2611
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=63, random_state=100,
subsample=0.65))])
cv score: [0.5625 0.58916667 0.69333333 0.67 0.70411392]
----------------------------------------
Trial 2612
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.9))])
cv score: [0.65666667 0.61333333 0.645 0.795 0.83227848]
----------------------------------------
Trial 2613
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=80, random_state=100,
subsample=0.8))])
cv score: [0.67 0.6175 0.7575 0.79083333 0.81882911]
----------------------------------------
Trial 2614
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
n_estimators=170, random_state=100,
subsample=0.6))])
cv score: [0.72416667 0.66583333 0.77333333 0.825 0.83781646]
----------------------------------------
Trial 2615
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=109, random_state=100,
subsample=0.95))])
cv score: [0.66583333 0.58083333 0.74333333 0.76833333 0.79193038]
----------------------------------------
Trial 2616
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=138,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63 0.6375 0.77583333 0.77583333 0.79351266]
----------------------------------------
Trial 2617
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=117,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6975 0.62166667 0.7425 0.785 0.8346519 ]
----------------------------------------
Trial 2618
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57083333 0.66416667 0.76166667 0.7175 0.79351266]
----------------------------------------
Trial 2619
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=28, random_state=100))])
cv score: [0.6975 0.64583333 0.70166667 0.80916667 0.90189873]
----------------------------------------
Trial 2620
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=169, random_state=100))])
cv score: [0.75 0.67666667 0.78166667 0.83333333 0.84889241]
----------------------------------------
Trial 2621
----------------------------------------
Parameters {'rf__n_estimators': 125, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=125, random_state=100))])
cv score: [0.76333333 0.695 0.78416667 0.855 0.87183544]
----------------------------------------
Trial 2622
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=126,
random_state=100))])
cv score: [0.58666667 0.66833333 0.705 0.83416667 0.82674051]
----------------------------------------
Trial 2623
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.74 0.78333333 0.87333333 0.85443038]
----------------------------------------
Trial 2624
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=83, random_state=100))])
cv score: [0.67625 0.63416667 0.725 0.81916667 0.80063291]
----------------------------------------
Trial 2625
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='sqrt',
n_estimators=196, random_state=100,
subsample=0.7))])
cv score: [0.7575 0.655 0.43833333 0.4225 0.61392405]
----------------------------------------
Trial 2626
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.73083333 0.79666667 0.85083333 0.8789557 ]
----------------------------------------
Trial 2627
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=42,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 2628
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='log2',
n_estimators=64, random_state=100,
subsample=0.6))])
cv score: [0.73166667 0.615 0.72416667 0.82083333 0.79193038]
----------------------------------------
Trial 2629
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=107,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.545 0.72125 0.6825 0.84041667 0.80735759]
----------------------------------------
Trial 2630
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=74, random_state=100))])
cv score: [0.72 0.68958333 0.7325 0.85416667 0.87737342]
----------------------------------------
Trial 2631
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=152, random_state=100,
subsample=0.75))])
cv score: [0.7175 0.68833333 0.75583333 0.7925 0.78243671]
----------------------------------------
Trial 2632
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=67,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62666667 0.67 0.7675 0.70333333 0.7056962 ]
----------------------------------------
Trial 2633
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=24, random_state=100,
subsample=0.8))])
cv score: [0.68583333 0.62083333 0.75916667 0.84166667 0.79825949]
----------------------------------------
Trial 2634
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=86, random_state=100))])
cv score: [0.73208333 0.66541667 0.78125 0.83 0.83148734]
----------------------------------------
Trial 2635
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=68,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67 0.6375 0.7875 0.77166667 0.81012658]
----------------------------------------
Trial 2636
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=146, random_state=100))])
cv score: [0.73583333 0.68041667 0.77833333 0.83583333 0.8346519 ]
----------------------------------------
Trial 2637
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=55,
random_state=100))])
cv score: [0.69166667 0.51791667 0.8 0.67125 0.62974684]
----------------------------------------
Trial 2638
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=121,
random_state=100))])
cv score: [0.69208333 0.4875 0.72041667 0.66791667 0.63647152]
----------------------------------------
Trial 2639
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7825 0.7325 0.775 0.85333333 0.8789557 ]
----------------------------------------
Trial 2640
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=199,
random_state=100))])
cv score: [0.66583333 0.50541667 0.79916667 0.66416667 0.78876582]
----------------------------------------
Trial 2641
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=183,
random_state=100))])
cv score: [0.62583333 0.67 0.70083333 0.81833333 0.82832278]
----------------------------------------
Trial 2642
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59666667 0.6375 0.735 0.6525 0.73655063]
----------------------------------------
Trial 2643
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=196, random_state=100))])
cv score: [0.68916667 0.61416667 0.72666667 0.82416667 0.82357595]
----------------------------------------
Trial 2644
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=124,
random_state=100))])
cv score: [0.65375 0.59166667 0.72125 0.78958333 0.76700949]
----------------------------------------
Trial 2645
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6325 0.75791667 0.77541667 0.84875 0.84256329]
----------------------------------------
Trial 2646
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=168, random_state=100,
subsample=0.7))])
cv score: [0.67666667 0.60416667 0.72666667 0.79583333 0.85205696]
----------------------------------------
Trial 2647
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=110, random_state=100))])
cv score: [0.61083333 0.6975 0.70416667 0.79833333 0.8306962 ]
----------------------------------------
Trial 2648
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.66333333 0.73166667 0.7775 0.82041139]
----------------------------------------
Trial 2649
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=170,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 2650
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='sqrt',
n_estimators=52, random_state=100,
subsample=0.8))])
cv score: [0.56833333 0.6575 0.6375 0.76333333 0.73971519]
----------------------------------------
Trial 2651
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=111, random_state=100))])
cv score: [0.77583333 0.68166667 0.75916667 0.85083333 0.86946203]
----------------------------------------
Trial 2652
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.665 0.72458333 0.69041667 0.86083333 0.83188291]
----------------------------------------
Trial 2653
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=45,
random_state=100))])
cv score: [0.62791667 0.69541667 0.72875 0.82791667 0.84295886]
----------------------------------------
Trial 2654
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=167, random_state=100))])
cv score: [0.7875 0.68166667 0.76333333 0.84833333 0.875 ]
----------------------------------------
Trial 2655
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=140,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.6925 0.80833333 0.84333333 0.88607595]
----------------------------------------
Trial 2656
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=126,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 2657
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='sqrt',
n_estimators=132, random_state=100,
subsample=0.65))])
cv score: [0.59083333 0.685 0.695 0.79833333 0.82832278]
----------------------------------------
Trial 2658
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=103, random_state=100))])
cv score: [0.5575 0.72333333 0.69 0.79583333 0.79193038]
----------------------------------------
Trial 2659
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.695 0.74541667 0.78458333 0.83041667 0.87816456]
----------------------------------------
Trial 2660
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=47, random_state=100,
subsample=0.95))])
cv score: [0.66833333 0.65291667 0.69708333 0.81666667 0.82515823]
----------------------------------------
Trial 2661
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=40, random_state=100))])
cv score: [0.69416667 0.61666667 0.75333333 0.81666667 0.81724684]
----------------------------------------
Trial 2662
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
n_estimators=36, random_state=100,
subsample=0.95))])
cv score: [0.74416667 0.63208333 0.82583333 0.8025 0.82120253]
----------------------------------------
Trial 2663
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='log2',
n_estimators=170, random_state=100,
subsample=0.75))])
cv score: [0.68583333 0.63666667 0.70083333 0.81833333 0.83939873]
----------------------------------------
Trial 2664
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56916667 0.66083333 0.74666667 0.73833333 0.78639241]
----------------------------------------
Trial 2665
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=134, random_state=100,
subsample=0.75))])
cv score: [0.43666667 0.69 0.57666667 0.48666667 0.70886076]
----------------------------------------
Trial 2666
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
n_estimators=25, random_state=100,
subsample=0.7))])
cv score: [0.75166667 0.67625 0.74875 0.84 0.88488924]
----------------------------------------
Trial 2667
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=10,
random_state=100))])
cv score: [0.51666667 0.59666667 0.64333333 0.85416667 0.81131329]
----------------------------------------
Trial 2668
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=88, random_state=100))])
cv score: [0.68041667 0.64166667 0.72416667 0.8225 0.81012658]
----------------------------------------
Trial 2669
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='log2',
n_estimators=43, random_state=100,
subsample=0.75))])
cv score: [0.7 0.60583333 0.69166667 0.76083333 0.8306962 ]
----------------------------------------
Trial 2670
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='log2',
n_estimators=163, random_state=100,
subsample=0.65))])
cv score: [0.68583333 0.62833333 0.695 0.81166667 0.82357595]
----------------------------------------
Trial 2671
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.725 0.79916667 0.85 0.89082278]
----------------------------------------
Trial 2672
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=151,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64416667 0.67083333 0.78083333 0.75666667 0.85047468]
----------------------------------------
Trial 2673
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='log2',
n_estimators=80, random_state=100,
subsample=0.6))])
cv score: [0.565 0.61 0.56 0.69166667 0.66297468]
----------------------------------------
Trial 2674
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76583333 0.71416667 0.8025 0.84333333 0.89003165]
----------------------------------------
Trial 2675
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=39,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75916667 0.745 0.795 0.855 0.88607595]
----------------------------------------
Trial 2676
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=179, random_state=100))])
cv score: [0.66333333 0.64833333 0.74 0.79916667 0.84493671]
----------------------------------------
Trial 2677
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=81,
random_state=100))])
cv score: [0.65333333 0.66166667 0.70333333 0.81083333 0.83702532]
----------------------------------------
Trial 2678
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=49, random_state=100))])
cv score: [0.65 0.67333333 0.72416667 0.7875 0.82990506]
----------------------------------------
Trial 2679
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=19,
random_state=100))])
cv score: [0.71166667 0.58208333 0.66 0.75833333 0.82080696]
----------------------------------------
Trial 2680
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=58, random_state=100,
subsample=0.85))])
cv score: [0.64416667 0.58583333 0.685 0.79666667 0.8346519 ]
----------------------------------------
Trial 2681
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.6875 0.78416667 0.82333333 0.85047468]
----------------------------------------
Trial 2682
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=74, random_state=100,
subsample=0.85))])
cv score: [0.76416667 0.63166667 0.805 0.81916667 0.80063291]
----------------------------------------
Trial 2683
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=180,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7225 0.68833333 0.79416667 0.79083333 0.85522152]
----------------------------------------
Trial 2684
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=114,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70333333 0.72041667 0.75666667 0.86916667 0.85363924]
----------------------------------------
Trial 2685
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=73,
random_state=100))])
cv score: [0.6975 0.62166667 0.68083333 0.7775 0.81882911]
----------------------------------------
Trial 2686
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=144, random_state=100,
subsample=0.6))])
cv score: [0.73666667 0.605 0.685 0.76666667 0.82436709]
----------------------------------------
Trial 2687
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=121, random_state=100))])
cv score: [0.76 0.69666667 0.78166667 0.8525 0.875 ]
----------------------------------------
Trial 2688
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=72,
random_state=100))])
cv score: [0.65083333 0.66083333 0.695 0.80083333 0.84731013]
----------------------------------------
Trial 2689
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65833333 0.625 0.78666667 0.72 0.76265823]
----------------------------------------
Trial 2690
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78833333 0.72666667 0.79333333 0.85166667 0.90031646]
----------------------------------------
Trial 2691
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=167, random_state=100))])
cv score: [0.7775 0.69583333 0.77416667 0.85416667 0.8710443 ]
----------------------------------------
Trial 2692
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=151,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.575 0.6675 0.7675 0.7475 0.81329114]
----------------------------------------
Trial 2693
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=81, random_state=100))])
cv score: [0.56416667 0.72875 0.69416667 0.78833333 0.79588608]
----------------------------------------
Trial 2694
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=155,
random_state=100))])
cv score: [0.53791667 0.66375 0.71041667 0.8225 0.78006329]
----------------------------------------
Trial 2695
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=40, random_state=100,
subsample=0.75))])
cv score: [0.675 0.66916667 0.68166667 0.8025 0.77848101]
----------------------------------------
Trial 2696
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=94,
random_state=100))])
cv score: [0.70333333 0.63208333 0.7175 0.80583333 0.8306962 ]
----------------------------------------
Trial 2697
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
n_estimators=130, random_state=100,
subsample=0.75))])
cv score: [0.74333333 0.67333333 0.725 0.75833333 0.74683544]
----------------------------------------
Trial 2698
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=121,
random_state=100))])
cv score: [0.615 0.6 0.68666667 0.67833333 0.70094937]
----------------------------------------
Trial 2699
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69583333 0.69166667 0.7925 0.78416667 0.87183544]
----------------------------------------
Trial 2700
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68166667 0.5825 0.73 0.77166667 0.78006329]
----------------------------------------
Trial 2701
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=63,
random_state=100,
subsample=0.95))])
cv score: [0.7625 0.66291667 0.80583333 0.8075 0.77768987]
----------------------------------------
Trial 2702
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=17,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.8175 0.735 0.79833333 0.84 0.84098101]
----------------------------------------
Trial 2703
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=186,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6225 0.6575 0.72916667 0.7575 0.79825949]
----------------------------------------
Trial 2704
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=161, random_state=100,
subsample=0.6))])
cv score: [0.76666667 0.65916667 0.77166667 0.84666667 0.84572785]
----------------------------------------
Trial 2705
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=24, random_state=100,
subsample=0.75))])
cv score: [0.68833333 0.6375 0.70083333 0.79166667 0.78481013]
----------------------------------------
Trial 2706
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.71916667 0.78583333 0.85083333 0.88132911]
----------------------------------------
Trial 2707
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=22, random_state=100))])
cv score: [0.63333333 0.64083333 0.71416667 0.75083333 0.82041139]
----------------------------------------
Trial 2708
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.72833333 0.76958333 0.84583333 0.84968354]
----------------------------------------
Trial 2709
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67416667 0.66916667 0.79083333 0.7925 0.85838608]
----------------------------------------
Trial 2710
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.65))])
cv score: [0.68166667 0.67916667 0.73583333 0.7775 0.84256329]
----------------------------------------
Trial 2711
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=38, random_state=100,
subsample=0.6))])
cv score: [0.7025 0.62666667 0.69416667 0.76333333 0.81566456]
----------------------------------------
Trial 2712
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=129,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67916667 0.67916667 0.76416667 0.755 0.82515823]
----------------------------------------
Trial 2713
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=194,
random_state=100))])
cv score: [0.71 0.60166667 0.67083333 0.80583333 0.84177215]
----------------------------------------
Trial 2714
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=133, random_state=100))])
cv score: [0.61166667 0.6975 0.70083333 0.80166667 0.82278481]
----------------------------------------
Trial 2715
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=49,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5875 0.71166667 0.70916667 0.7025 0.86313291]
----------------------------------------
Trial 2716
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=182,
random_state=100))])
cv score: [0.63875 0.57125 0.73375 0.79 0.79588608]
----------------------------------------
Trial 2717
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7425 0.71583333 0.8 0.8625 0.88370253]
----------------------------------------
Trial 2718
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=192,
random_state=100))])
cv score: [0.63666667 0.57458333 0.73 0.7925 0.80379747]
----------------------------------------
Trial 2719
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=70,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78416667 0.73083333 0.7975 0.85333333 0.88528481]
----------------------------------------
Trial 2720
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=159, random_state=100,
subsample=0.6))])
cv score: [0.61416667 0.59833333 0.67833333 0.77583333 0.73259494]
----------------------------------------
Trial 2721
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='sqrt',
n_estimators=179, random_state=100,
subsample=0.7))])
cv score: [0.64833333 0.5875 0.66166667 0.71583333 0.69541139]
----------------------------------------
Trial 2722
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=35,
random_state=100))])
cv score: [0.69125 0.60458333 0.74166667 0.77333333 0.76700949]
----------------------------------------
Trial 2723
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=184,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77916667 0.73583333 0.78333333 0.84083333 0.87420886]
----------------------------------------
Trial 2724
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=131,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7775 0.7325 0.79333333 0.85083333 0.86075949]
----------------------------------------
Trial 2725
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=197,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76083333 0.74916667 0.78833333 0.8575 0.84572785]
----------------------------------------
Trial 2726
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72666667 0.715 0.7475 0.84916667 0.86629747]
----------------------------------------
Trial 2727
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68708333 0.71625 0.77208333 0.85083333 0.84335443]
----------------------------------------
Trial 2728
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=130, random_state=100))])
cv score: [0.70458333 0.615 0.76375 0.81541667 0.79549051]
----------------------------------------
Trial 2729
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.65))])
cv score: [0.6275 0.64083333 0.70416667 0.81 0.86550633]
----------------------------------------
Trial 2730
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73083333 0.73208333 0.75833333 0.85583333 0.8931962 ]
----------------------------------------
Trial 2731
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69666667 0.74833333 0.77333333 0.865 0.84810127]
----------------------------------------
Trial 2732
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
n_estimators=147, random_state=100,
subsample=0.95))])
cv score: [0.7575 0.67833333 0.76583333 0.83083333 0.77689873]
----------------------------------------
Trial 2733
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=137, random_state=100))])
cv score: [0.70458333 0.61708333 0.75666667 0.82041667 0.79232595]
----------------------------------------
Trial 2734
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=198, random_state=100,
subsample=0.8))])
cv score: [0.76083333 0.68458333 0.73291667 0.855 0.87974684]
----------------------------------------
Trial 2735
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=29,
random_state=100))])
cv score: [0.75416667 0.58791667 0.80875 0.78833333 0.80379747]
----------------------------------------
Trial 2736
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
n_estimators=146, random_state=100,
subsample=0.7))])
cv score: [0.74916667 0.68333333 0.75333333 0.85916667 0.90031646]
----------------------------------------
Trial 2737
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=36, random_state=100))])
cv score: [0.61 0.72375 0.69625 0.82666667 0.87539557]
----------------------------------------
Trial 2738
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=84, random_state=100))])
cv score: [0.695 0.60833333 0.72333333 0.7875 0.8306962 ]
----------------------------------------
Trial 2739
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=117,
random_state=100))])
cv score: [0.82 0.57625 0.73291667 0.76333333 0.67167722]
----------------------------------------
Trial 2740
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=17, random_state=100))])
cv score: [0.60666667 0.66 0.67625 0.79916667 0.82120253]
----------------------------------------
Trial 2741
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66583333 0.5925 0.78916667 0.775 0.80775316]
----------------------------------------
Trial 2742
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=129, random_state=100))])
cv score: [0.69 0.62666667 0.7375 0.79166667 0.82357595]
----------------------------------------
Trial 2743
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=68, random_state=100,
subsample=0.8))])
cv score: [0.68333333 0.6675 0.76583333 0.78416667 0.84493671]
----------------------------------------
Trial 2744
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=185, random_state=100,
subsample=0.9))])
cv score: [0.63583333 0.63833333 0.69416667 0.7775 0.67325949]
----------------------------------------
Trial 2745
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=30, random_state=100,
subsample=0.75))])
cv score: [0.67666667 0.66041667 0.8175 0.83083333 0.83306962]
----------------------------------------
Trial 2746
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=188, random_state=100))])
cv score: [0.71333333 0.67583333 0.77416667 0.84 0.83702532]
----------------------------------------
Trial 2747
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='sqrt',
n_estimators=26, random_state=100,
subsample=0.8))])
cv score: [0.74166667 0.74541667 0.72416667 0.85416667 0.81606013]
----------------------------------------
Trial 2748
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=37, random_state=100,
subsample=0.7))])
cv score: [0.68333333 0.6725 0.67833333 0.8175 0.84810127]
----------------------------------------
Trial 2749
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=85, random_state=100))])
cv score: [0.6975 0.61083333 0.72333333 0.78833333 0.83148734]
----------------------------------------
Trial 2750
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=116,
random_state=100, subsample=0.9))])
cv score: [0.72916667 0.64416667 0.76916667 0.84166667 0.78639241]
----------------------------------------
Trial 2751
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=104,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68083333 0.65333333 0.74916667 0.75583333 0.7681962 ]
----------------------------------------
Trial 2752
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=159, random_state=100,
subsample=0.85))])
cv score: [0.6875 0.67166667 0.72583333 0.8025 0.7903481 ]
----------------------------------------
Trial 2753
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=117,
random_state=100))])
cv score: [0.75166667 0.58791667 0.80916667 0.79041667 0.80498418]
----------------------------------------
Trial 2754
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='sqrt',
n_estimators=124, random_state=100,
subsample=0.8))])
cv score: [0.675 0.59916667 0.73416667 0.835 0.77689873]
----------------------------------------
Trial 2755
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.72583333 0.77291667 0.8625 0.86787975]
----------------------------------------
Trial 2756
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=167,
random_state=100))])
cv score: [0.68583333 0.59166667 0.70166667 0.79666667 0.81012658]
----------------------------------------
Trial 2757
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='sqrt',
n_estimators=193, random_state=100,
subsample=0.9))])
cv score: [0.67833333 0.60833333 0.70666667 0.76333333 0.82041139]
----------------------------------------
Trial 2758
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=108, random_state=100,
subsample=0.95))])
cv score: [0.74916667 0.6425 0.71833333 0.74916667 0.73101266]
----------------------------------------
Trial 2759
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63 0.68 0.69916667 0.75916667 0.78639241]
----------------------------------------
Trial 2760
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=71, random_state=100))])
cv score: [0.6975 0.66416667 0.73333333 0.79333333 0.86946203]
----------------------------------------
Trial 2761
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66666667 0.65166667 0.7575 0.71583333 0.7903481 ]
----------------------------------------
Trial 2762
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=82,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.7175 0.77541667 0.86666667 0.84810127]
----------------------------------------
Trial 2763
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74083333 0.71416667 0.8025 0.825 0.86471519]
----------------------------------------
Trial 2764
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63666667 0.65583333 0.75 0.74083333 0.77531646]
----------------------------------------
Trial 2765
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=131,
random_state=100))])
cv score: [0.685 0.61 0.69666667 0.77333333 0.74525316]
----------------------------------------
Trial 2766
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=132,
random_state=100))])
cv score: [0.75 0.58791667 0.80875 0.79041667 0.80458861]
----------------------------------------
Trial 2767
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
n_estimators=106, random_state=100,
subsample=0.9))])
cv score: [0.6025 0.68291667 0.66041667 0.77708333 0.78125 ]
----------------------------------------
Trial 2768
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=21, random_state=100,
subsample=0.6))])
cv score: [0.69083333 0.66166667 0.7 0.7625 0.86234177]
----------------------------------------
Trial 2769
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=78,
random_state=100))])
cv score: [0.69375 0.51791667 0.79916667 0.67 0.62935127]
----------------------------------------
Trial 2770
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70333333 0.58583333 0.78416667 0.78666667 0.81882911]
----------------------------------------
Trial 2771
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=92,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72666667 0.70916667 0.8 0.8225 0.8306962 ]
----------------------------------------
Trial 2772
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=23, random_state=100))])
cv score: [0.7 0.6475 0.66916667 0.795 0.85522152]
----------------------------------------
Trial 2773
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=62,
random_state=100))])
cv score: [0.64416667 0.65583333 0.6925 0.805 0.84177215]
----------------------------------------
Trial 2774
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
n_estimators=94, random_state=100,
subsample=0.85))])
cv score: [0.7275 0.63083333 0.8 0.82083333 0.80537975]
----------------------------------------
Trial 2775
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=75, random_state=100))])
cv score: [0.6525 0.65583333 0.70416667 0.82083333 0.84968354]
----------------------------------------
Trial 2776
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=83,
random_state=100))])
cv score: [0.51708333 0.67041667 0.7175 0.81666667 0.75791139]
----------------------------------------
Trial 2777
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=139, random_state=100))])
cv score: [0.75583333 0.67791667 0.78666667 0.835 0.84651899]
----------------------------------------
Trial 2778
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78083333 0.7375 0.80083333 0.8425 0.86629747]
----------------------------------------
Trial 2779
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=181, random_state=100,
subsample=0.8))])
cv score: [0.73166667 0.65583333 0.78083333 0.84083333 0.81724684]
----------------------------------------
Trial 2780
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=152,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68083333 0.66166667 0.77583333 0.78833333 0.82515823]
----------------------------------------
Trial 2781
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=199, random_state=100,
subsample=0.8))])
cv score: [0.63333333 0.685 0.75583333 0.71 0.70886076]
----------------------------------------
Trial 2782
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=198,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.675 0.65083333 0.73833333 0.77416667 0.8710443 ]
----------------------------------------
Trial 2783
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.7375 0.80166667 0.865 0.87025316]
----------------------------------------
Trial 2784
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=67, random_state=100))])
cv score: [0.61333333 0.68416667 0.63416667 0.81833333 0.8164557 ]
----------------------------------------
Trial 2785
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=65,
random_state=100))])
cv score: [0.69416667 0.67916667 0.7075 0.81125 0.83544304]
----------------------------------------
Trial 2786
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=37, random_state=100))])
cv score: [0.72375 0.6675 0.77916667 0.83666667 0.83583861]
----------------------------------------
Trial 2787
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=132,
random_state=100))])
cv score: [0.69 0.63333333 0.70166667 0.8075 0.82594937]
----------------------------------------
Trial 2788
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=34,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69833333 0.71375 0.75625 0.85333333 0.84968354]
----------------------------------------
Trial 2789
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=187,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77166667 0.71833333 0.78333333 0.86583333 0.8931962 ]
----------------------------------------
Trial 2790
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
n_estimators=81, random_state=100,
subsample=0.75))])
cv score: [0.73666667 0.6475 0.77916667 0.83083333 0.83227848]
----------------------------------------
Trial 2791
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.695 0.725 0.76333333 0.80833333 0.89794304]
----------------------------------------
Trial 2792
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=60, random_state=100,
subsample=0.8))])
cv score: [0.71583333 0.72083333 0.68916667 0.80833333 0.78243671]
----------------------------------------
Trial 2793
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=140,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77416667 0.72625 0.76125 0.85416667 0.85957278]
----------------------------------------
Trial 2794
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.745 0.72833333 0.8125 0.8175 0.88291139]
----------------------------------------
Trial 2795
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73 0.6825 0.78333333 0.80333333 0.85363924]
----------------------------------------
Trial 2796
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='sqrt',
n_estimators=184, random_state=100,
subsample=0.6))])
cv score: [0.6825 0.595 0.66583333 0.80833333 0.78955696]
----------------------------------------
Trial 2797
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.8))])
cv score: [0.65583333 0.63583333 0.715 0.80916667 0.85047468]
----------------------------------------
Trial 2798
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=116, random_state=100,
subsample=0.7))])
cv score: [0.62666667 0.70583333 0.68166667 0.765 0.86867089]
----------------------------------------
Trial 2799
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=37,
random_state=100))])
cv score: [0.68666667 0.57625 0.71083333 0.82166667 0.82753165]
----------------------------------------
Trial 2800
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=50, random_state=100))])
cv score: [0.62291667 0.64166667 0.735 0.84166667 0.77887658]
----------------------------------------
Trial 2801
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=188,
random_state=100,
subsample=0.95))])
cv score: [0.75 0.61916667 0.74666667 0.7775 0.76107595]
----------------------------------------
Trial 2802
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=160, random_state=100))])
cv score: [0.68125 0.63541667 0.745 0.82166667 0.82278481]
----------------------------------------
Trial 2803
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=67,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68 0.62333333 0.79333333 0.77333333 0.84018987]
----------------------------------------
Trial 2804
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.52791667 0.71291667 0.68041667 0.80041667 0.73536392]
----------------------------------------
Trial 2805
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=194,
random_state=100))])
cv score: [0.62333333 0.66166667 0.705 0.82083333 0.82753165]
----------------------------------------
Trial 2806
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='sqrt',
n_estimators=134, random_state=100,
subsample=0.65))])
cv score: [0.58666667 0.68083333 0.70083333 0.79833333 0.82674051]
----------------------------------------
Trial 2807
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=191, random_state=100,
subsample=0.65))])
cv score: [0.62833333 0.6675 0.70833333 0.84166667 0.82515823]
----------------------------------------
Trial 2808
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=159, random_state=100))])
cv score: [0.61 0.68916667 0.70333333 0.8 0.8346519 ]
----------------------------------------
Trial 2809
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=16, random_state=100))])
cv score: [0.7325 0.69625 0.75 0.86666667 0.87341772]
----------------------------------------
Trial 2810
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=186, random_state=100))])
cv score: [0.66333333 0.65166667 0.73916667 0.80583333 0.84256329]
----------------------------------------
Trial 2811
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=112, random_state=100,
subsample=0.85))])
cv score: [0.64416667 0.59833333 0.68416667 0.80666667 0.79667722]
----------------------------------------
Trial 2812
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=175,
random_state=100))])
cv score: [0.6825 0.58916667 0.69916667 0.79916667 0.81091772]
----------------------------------------
Trial 2813
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.685 0.66416667 0.7925 0.78 0.87341772]
----------------------------------------
Trial 2814
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=170,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56 0.72958333 0.70958333 0.81666667 0.7772943 ]
----------------------------------------
Trial 2815
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.8))])
cv score: [0.58583333 0.59 0.7175 0.72666667 0.70490506]
----------------------------------------
Trial 2816
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=187, random_state=100))])
cv score: [0.72333333 0.67291667 0.77875 0.83875 0.82199367]
----------------------------------------
Trial 2817
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=104,
random_state=100))])
cv score: [0.64916667 0.66416667 0.6975 0.80916667 0.84177215]
----------------------------------------
Trial 2818
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.74125 0.78458333 0.83833333 0.85443038]
----------------------------------------
Trial 2819
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=11, random_state=100))])
cv score: [0.65125 0.74333333 0.64958333 0.78625 0.89042722]
----------------------------------------
Trial 2820
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='sqrt',
n_estimators=165, random_state=100,
subsample=0.65))])
cv score: [0.6125 0.67333333 0.6975 0.84416667 0.80221519]
----------------------------------------
Trial 2821
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='sqrt',
n_estimators=91, random_state=100,
subsample=0.8))])
cv score: [0.63083333 0.6375 0.67166667 0.82583333 0.83148734]
----------------------------------------
Trial 2822
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=110,
random_state=100))])
cv score: [0.63875 0.57916667 0.73625 0.78083333 0.78085443]
----------------------------------------
Trial 2823
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=31,
random_state=100))])
cv score: [0.55333333 0.65083333 0.63833333 0.66416667 0.72943038]
----------------------------------------
Trial 2824
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70708333 0.73375 0.70916667 0.8525 0.82753165]
----------------------------------------
Trial 2825
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66916667 0.65 0.79583333 0.74666667 0.79825949]
----------------------------------------
Trial 2826
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
n_estimators=163, random_state=100,
subsample=0.7))])
cv score: [0.77416667 0.6675 0.77666667 0.83916667 0.8568038 ]
----------------------------------------
Trial 2827
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=141, random_state=100,
subsample=0.85))])
cv score: [0.55333333 0.58083333 0.72 0.695 0.66060127]
----------------------------------------
Trial 2828
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.72541667 0.77333333 0.85833333 0.86234177]
----------------------------------------
Trial 2829
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=83, random_state=100,
subsample=0.85))])
cv score: [0.73166667 0.65416667 0.8225 0.82583333 0.78797468]
----------------------------------------
Trial 2830
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=120, random_state=100,
subsample=0.9))])
cv score: [0.6975 0.6625 0.75416667 0.7775 0.79984177]
----------------------------------------
Trial 2831
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=104,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7575 0.71583333 0.78916667 0.85083333 0.88291139]
----------------------------------------
Trial 2832
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=117,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.64666667 0.79333333 0.77416667 0.8306962 ]
----------------------------------------
Trial 2833
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=54,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.66333333 0.74333333 0.75083333 0.79272152]
----------------------------------------
Trial 2834
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=130,
random_state=100))])
cv score: [0.69333333 0.5975 0.67416667 0.80583333 0.83860759]
----------------------------------------
Trial 2835
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=51, random_state=100,
subsample=0.65))])
cv score: [0.6625 0.6575 0.72333333 0.81583333 0.82832278]
----------------------------------------
Trial 2836
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='log2',
n_estimators=123, random_state=100,
subsample=0.7))])
cv score: [0.53333333 0.7 0.7 0.6175 0.71914557]
----------------------------------------
Trial 2837
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=131, random_state=100,
subsample=0.7))])
cv score: [0.745 0.735 0.76916667 0.83583333 0.8306962 ]
----------------------------------------
Trial 2838
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=136, random_state=100,
subsample=0.8))])
cv score: [0.73166667 0.6825 0.78583333 0.83833333 0.80617089]
----------------------------------------
Trial 2839
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=15, random_state=100))])
cv score: [0.59166667 0.64916667 0.69833333 0.795 0.81962025]
----------------------------------------
Trial 2840
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=157, random_state=100))])
cv score: [0.6825 0.63083333 0.745 0.81583333 0.8215981 ]
----------------------------------------
Trial 2841
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69333333 0.6675 0.7925 0.78166667 0.84968354]
----------------------------------------
Trial 2842
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=51, random_state=100))])
cv score: [0.71958333 0.66416667 0.78375 0.83291667 0.83425633]
----------------------------------------
Trial 2843
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=159, random_state=100,
subsample=0.65))])
cv score: [0.66916667 0.61166667 0.70416667 0.7925 0.82911392]
----------------------------------------
Trial 2844
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='sqrt',
n_estimators=47, random_state=100,
subsample=0.85))])
cv score: [0.60333333 0.59333333 0.71666667 0.70583333 0.71360759]
----------------------------------------
Trial 2845
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71458333 0.70375 0.75541667 0.84916667 0.8346519 ]
----------------------------------------
Trial 2846
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66666667 0.635 0.79166667 0.76916667 0.78797468]
----------------------------------------
Trial 2847
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.71458333 0.79375 0.81916667 0.81962025]
----------------------------------------
Trial 2848
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=82,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68416667 0.67166667 0.775 0.80166667 0.88449367]
----------------------------------------
Trial 2849
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77083333 0.72125 0.78333333 0.83583333 0.84651899]
----------------------------------------
Trial 2850
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=131,
random_state=100))])
cv score: [0.76458333 0.49375 0.7875 0.70208333 0.79232595]
----------------------------------------
Trial 2851
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=187,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63333333 0.57666667 0.75916667 0.745 0.80221519]
----------------------------------------
Trial 2852
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.72208333 0.76375 0.8475 0.86867089]
----------------------------------------
Trial 2853
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=194, random_state=100,
subsample=0.6))])
cv score: [0.675 0.63666667 0.7075 0.80083333 0.82832278]
----------------------------------------
Trial 2854
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61666667 0.70666667 0.7425 0.74083333 0.84810127]
----------------------------------------
Trial 2855
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=155,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64666667 0.61916667 0.74333333 0.73333333 0.79746835]
----------------------------------------
Trial 2856
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=153,
random_state=100))])
cv score: [0.68583333 0.68666667 0.69666667 0.7525 0.63053797]
----------------------------------------
Trial 2857
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=49,
random_state=100))])
cv score: [0.62791667 0.59958333 0.74208333 0.74458333 0.82436709]
----------------------------------------
Trial 2858
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=54, random_state=100,
subsample=0.95))])
cv score: [0.6625 0.62 0.7275 0.7775 0.78006329]
----------------------------------------
Trial 2859
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=53, random_state=100))])
cv score: [0.71791667 0.6925 0.715 0.8475 0.87816456]
----------------------------------------
Trial 2860
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=41,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.7225 0.8025 0.81166667 0.88607595]
----------------------------------------
Trial 2861
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=38, random_state=100,
subsample=0.85))])
cv score: [0.67583333 0.665 0.73916667 0.68333333 0.74446203]
----------------------------------------
Trial 2862
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6925 0.74375 0.7625 0.85583333 0.86708861]
----------------------------------------
Trial 2863
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='log2',
n_estimators=25, random_state=100,
subsample=0.7))])
cv score: [0.6325 0.6275 0.73666667 0.81666667 0.85759494]
----------------------------------------
Trial 2864
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=33, random_state=100,
subsample=0.6))])
cv score: [0.57916667 0.585 0.5225 0.72166667 0.52531646]
----------------------------------------
Trial 2865
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='log2',
n_estimators=79, random_state=100,
subsample=0.65))])
cv score: [0.58583333 0.63416667 0.67 0.8 0.7721519 ]
----------------------------------------
Trial 2866
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=87,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75833333 0.72541667 0.77333333 0.86166667 0.90743671]
----------------------------------------
Trial 2867
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74666667 0.7175 0.79833333 0.86166667 0.88449367]
----------------------------------------
Trial 2868
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=54, random_state=100,
subsample=0.6))])
cv score: [0.66916667 0.695 0.70416667 0.8375 0.84177215]
----------------------------------------
Trial 2869
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.7525 0.78333333 0.87083333 0.8568038 ]
----------------------------------------
Trial 2870
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=182, random_state=100,
subsample=0.75))])
cv score: [0.71416667 0.64 0.76666667 0.76583333 0.81170886]
----------------------------------------
Trial 2871
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74583333 0.69833333 0.75333333 0.835 0.90506329]
----------------------------------------
Trial 2872
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.685 0.66916667 0.77666667 0.81833333 0.82120253]
----------------------------------------
Trial 2873
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=93,
random_state=100))])
cv score: [0.67833333 0.60333333 0.7175 0.80583333 0.82278481]
----------------------------------------
Trial 2874
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=190,
random_state=100))])
cv score: [0.63916667 0.57166667 0.72916667 0.78875 0.80221519]
----------------------------------------
Trial 2875
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='sqrt',
n_estimators=134, random_state=100,
subsample=0.7))])
cv score: [0.66166667 0.61916667 0.72583333 0.805 0.80300633]
----------------------------------------
Trial 2876
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73333333 0.685 0.78833333 0.81416667 0.87658228]
----------------------------------------
Trial 2877
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.65))])
cv score: [0.6125 0.57583333 0.6525 0.55416667 0.71993671]
----------------------------------------
Trial 2878
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=72,
random_state=100))])
cv score: [0.65083333 0.66083333 0.695 0.80083333 0.84731013]
----------------------------------------
Trial 2879
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=10, random_state=100,
subsample=0.75))])
cv score: [0.64083333 0.71083333 0.56583333 0.6825 0.82911392]
----------------------------------------
Trial 2880
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=93, random_state=100))])
cv score: [0.67833333 0.63958333 0.72875 0.82333333 0.81012658]
----------------------------------------
Trial 2881
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60833333 0.64833333 0.735 0.78666667 0.83702532]
----------------------------------------
Trial 2882
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=126,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.68 0.795 0.8 0.88370253]
----------------------------------------
Trial 2883
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=123, random_state=100))])
cv score: [0.69416667 0.6325 0.72166667 0.8025 0.84968354]
----------------------------------------
Trial 2884
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=138,
random_state=100))])
cv score: [0.64666667 0.58333333 0.67666667 0.70833333 0.67325949]
----------------------------------------
Trial 2885
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=15,
random_state=100))])
cv score: [0.69166667 0.50208333 0.75416667 0.675 0.63686709]
----------------------------------------
Trial 2886
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69583333 0.67 0.79916667 0.78416667 0.8528481 ]
----------------------------------------
Trial 2887
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
n_estimators=38, random_state=100,
subsample=0.6))])
cv score: [0.75416667 0.69333333 0.78041667 0.83 0.84018987]
----------------------------------------
Trial 2888
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=161, random_state=100))])
cv score: [0.72333333 0.67333333 0.77833333 0.83458333 0.83148734]
----------------------------------------
Trial 2889
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=168, random_state=100))])
cv score: [0.5725 0.70166667 0.705 0.81416667 0.8164557 ]
----------------------------------------
Trial 2890
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=155,
random_state=100,
subsample=0.95))])
cv score: [0.655 0.60583333 0.685 0.74 0.71202532]
----------------------------------------
Trial 2891
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67083333 0.68 0.78 0.8025 0.84810127]
----------------------------------------
Trial 2892
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=184,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68833333 0.62666667 0.72916667 0.76166667 0.8346519 ]
----------------------------------------
Trial 2893
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=197, random_state=100,
subsample=0.8))])
cv score: [0.60583333 0.5825 0.64083333 0.73083333 0.76582278]
----------------------------------------
Trial 2894
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=128,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6225 0.66333333 0.75083333 0.77333333 0.80775316]
----------------------------------------
Trial 2895
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=47,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83666667 0.82199367]
----------------------------------------
Trial 2896
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=19,
random_state=100))])
cv score: [0.6625 0.605 0.72875 0.80208333 0.77689873]
----------------------------------------
Trial 2897
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=47,
random_state=100))])
cv score: [0.69 0.4875 0.72208333 0.67041667 0.63528481]
----------------------------------------
Trial 2898
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69125 0.75083333 0.75833333 0.83958333 0.88528481]
----------------------------------------
Trial 2899
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=151,
random_state=100))])
cv score: [0.62583333 0.57583333 0.7125 0.74916667 0.7318038 ]
----------------------------------------
Trial 2900
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=27, random_state=100))])
cv score: [0.66708333 0.64041667 0.70416667 0.80041667 0.82199367]
----------------------------------------
Trial 2901
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=54,
random_state=100))])
cv score: [0.69 0.48916667 0.77583333 0.6725 0.63844937]
----------------------------------------
Trial 2902
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
n_estimators=178, random_state=100,
subsample=0.8))])
cv score: [0.68166667 0.64333333 0.70583333 0.78166667 0.76028481]
----------------------------------------
Trial 2903
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
n_estimators=94, random_state=100,
subsample=0.8))])
cv score: [0.71583333 0.66333333 0.7675 0.8275 0.8125 ]
----------------------------------------
Trial 2904
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=71,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77666667 0.72125 0.76083333 0.85916667 0.91376582]
----------------------------------------
Trial 2905
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='log2',
n_estimators=133, random_state=100,
subsample=0.65))])
cv score: [0.69583333 0.65166667 0.72416667 0.8075 0.82594937]
----------------------------------------
Trial 2906
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=137, random_state=100,
subsample=0.85))])
cv score: [0.60833333 0.54333333 0.6775 0.77333333 0.78085443]
----------------------------------------
Trial 2907
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64583333 0.6675 0.73666667 0.71166667 0.70648734]
----------------------------------------
Trial 2908
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=39, random_state=100,
subsample=0.6))])
cv score: [0.70083333 0.47 0.675 0.67083333 0.48813291]
----------------------------------------
Trial 2909
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=24, random_state=100,
subsample=0.85))])
cv score: [0.64333333 0.61416667 0.76916667 0.765 0.74050633]
----------------------------------------
Trial 2910
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=35,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61833333 0.6575 0.745 0.71416667 0.8085443 ]
----------------------------------------
Trial 2911
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=168, random_state=100))])
cv score: [0.6725 0.66 0.71583333 0.81 0.85759494]
----------------------------------------
Trial 2912
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65083333 0.67583333 0.755 0.76916667 0.81566456]
----------------------------------------
Trial 2913
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=104,
random_state=100))])
cv score: [0.64125 0.5825 0.735 0.7775 0.78599684]
----------------------------------------
Trial 2914
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=193, random_state=100,
subsample=0.7))])
cv score: [0.6875 0.59166667 0.72916667 0.7875 0.82357595]
----------------------------------------
Trial 2915
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=18, random_state=100,
subsample=0.95))])
cv score: [0.54833333 0.6675 0.70708333 0.79416667 0.75593354]
----------------------------------------
Trial 2916
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=166,
random_state=100))])
cv score: [0.70916667 0.605 0.67416667 0.80416667 0.83860759]
----------------------------------------
Trial 2917
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=41,
random_state=100))])
cv score: [0.67916667 0.58541667 0.69833333 0.80166667 0.82674051]
----------------------------------------
Trial 2918
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=145, random_state=100,
subsample=0.6))])
cv score: [0.705 0.68 0.75333333 0.83666667 0.86313291]
----------------------------------------
Trial 2919
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='log2',
n_estimators=146, random_state=100,
subsample=0.75))])
cv score: [0.56416667 0.54916667 0.6575 0.62333333 0.72072785]
----------------------------------------
Trial 2920
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=45,
random_state=100))])
cv score: [0.58791667 0.68041667 0.70125 0.82875 0.83148734]
----------------------------------------
Trial 2921
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=53, random_state=100))])
cv score: [0.675 0.6275 0.75416667 0.81083333 0.82594937]
----------------------------------------
Trial 2922
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=99, random_state=100))])
cv score: [0.7475 0.69041667 0.7125 0.855 0.87658228]
----------------------------------------
Trial 2923
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=13,
random_state=100))])
cv score: [0.58083333 0.58416667 0.66041667 0.82791667 0.84968354]
----------------------------------------
Trial 2924
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=85,
random_state=100))])
cv score: [0.64083333 0.58416667 0.69916667 0.80666667 0.81170886]
----------------------------------------
Trial 2925
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=70,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53791667 0.65375 0.6725 0.81083333 0.77452532]
----------------------------------------
Trial 2926
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73166667 0.7475 0.78166667 0.86083333 0.85917722]
----------------------------------------
Trial 2927
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=186,
random_state=100))])
cv score: [0.68708333 0.49375 0.81 0.64666667 0.75316456]
----------------------------------------
Trial 2928
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, n_estimators=183,
random_state=100, subsample=0.7))])
cv score: [0.705 0.66166667 0.76 0.8175 0.76898734]
----------------------------------------
Trial 2929
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7025 0.68 0.74166667 0.74833333 0.77373418]
----------------------------------------
Trial 2930
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7375 0.72416667 0.7575 0.85666667 0.88924051]
----------------------------------------
Trial 2931
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72833333 0.6525 0.78166667 0.76083333 0.84335443]
----------------------------------------
Trial 2932
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=54, random_state=100))])
cv score: [0.61166667 0.68 0.70625 0.79916667 0.83227848]
----------------------------------------
Trial 2933
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=16,
random_state=100))])
cv score: [0.67666667 0.55041667 0.7 0.82958333 0.87183544]
----------------------------------------
Trial 2934
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=22, random_state=100))])
cv score: [0.64166667 0.66166667 0.70166667 0.71958333 0.80063291]
----------------------------------------
Trial 2935
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='log2',
n_estimators=113, random_state=100,
subsample=0.75))])
cv score: [0.60666667 0.6 0.64416667 0.70916667 0.65901899]
----------------------------------------
Trial 2936
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='sqrt',
n_estimators=133, random_state=100,
subsample=0.75))])
cv score: [0.71083333 0.60333333 0.68 0.72416667 0.72389241]
----------------------------------------
Trial 2937
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='log2',
n_estimators=170, random_state=100,
subsample=0.6))])
cv score: [0.63416667 0.62 0.6125 0.66916667 0.69462025]
----------------------------------------
Trial 2938
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.95))])
cv score: [0.6675 0.61916667 0.67666667 0.81 0.78639241]
----------------------------------------
Trial 2939
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.62 0.7925 0.74 0.77294304]
----------------------------------------
Trial 2940
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, n_estimators=14,
random_state=100, subsample=0.9))])
cv score: [0.75958333 0.72541667 0.77208333 0.84041667 0.875 ]
----------------------------------------
Trial 2941
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.55791667 0.60666667 0.64458333 0.8025 0.71004747]
----------------------------------------
Trial 2942
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=87, random_state=100,
subsample=0.65))])
cv score: [0.6825 0.71833333 0.68166667 0.78083333 0.78797468]
----------------------------------------
Trial 2943
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=106, random_state=100,
subsample=0.65))])
cv score: [0.65333333 0.52416667 0.6825 0.71416667 0.61075949]
----------------------------------------
Trial 2944
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='sqrt',
n_estimators=101, random_state=100,
subsample=0.9))])
cv score: [0.62833333 0.61583333 0.67416667 0.75166667 0.74525316]
----------------------------------------
Trial 2945
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=55, random_state=100))])
cv score: [0.72 0.67791667 0.77833333 0.83208333 0.84414557]
----------------------------------------
Trial 2946
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=188, random_state=100,
subsample=0.75))])
cv score: [0.64666667 0.66166667 0.7125 0.82583333 0.83939873]
----------------------------------------
Trial 2947
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=76,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63416667 0.575 0.75416667 0.77 0.74841772]
----------------------------------------
Trial 2948
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7675 0.72 0.73333333 0.8575 0.86234177]
----------------------------------------
Trial 2949
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='log2',
n_estimators=170,
random_state=100))])
cv score: [0.675 0.58833333 0.71 0.77416667 0.76977848]
----------------------------------------
Trial 2950
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=157, random_state=100))])
cv score: [0.70583333 0.645 0.73 0.79916667 0.82911392]
----------------------------------------
Trial 2951
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=77,
random_state=100))])
cv score: [0.70416667 0.61166667 0.76958333 0.76833333 0.71795886]
----------------------------------------
Trial 2952
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=50, random_state=100))])
cv score: [0.615 0.66833333 0.7125 0.765 0.82753165]
----------------------------------------
Trial 2953
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='log2',
n_estimators=76,
random_state=100))])
cv score: [0.67666667 0.58416667 0.68916667 0.80666667 0.7721519 ]
----------------------------------------
Trial 2954
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=22,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.62083333 0.735 0.67083333 0.7681962 ]
----------------------------------------
Trial 2955
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7 0.66666667 0.80083333 0.78 0.84889241]
----------------------------------------
Trial 2956
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=47, random_state=100))])
cv score: [0.75833333 0.68708333 0.75083333 0.8425 0.88924051]
----------------------------------------
Trial 2957
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=21, random_state=100))])
cv score: [0.6625 0.55583333 0.73916667 0.73708333 0.8215981 ]
----------------------------------------
Trial 2958
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=27,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59166667 0.7375 0.73583333 0.82666667 0.80498418]
----------------------------------------
Trial 2959
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=76,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65416667 0.64916667 0.75416667 0.74083333 0.78955696]
----------------------------------------
Trial 2960
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71416667 0.68916667 0.79583333 0.79916667 0.85205696]
----------------------------------------
Trial 2961
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=20,
random_state=100))])
cv score: [0.56083333 0.66125 0.66625 0.82916667 0.81685127]
----------------------------------------
Trial 2962
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=57,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71416667 0.68 0.745 0.76333333 0.8085443 ]
----------------------------------------
Trial 2963
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75 0.68666667 0.7725 0.78833333 0.8568038 ]
----------------------------------------
Trial 2964
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=98, random_state=100,
subsample=0.85))])
cv score: [0.7 0.61416667 0.73583333 0.75166667 0.67484177]
----------------------------------------
Trial 2965
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=38, random_state=100))])
cv score: [0.68583333 0.67583333 0.6825 0.80916667 0.86787975]
----------------------------------------
Trial 2966
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=58,
random_state=100))])
cv score: [0.64333333 0.60625 0.71875 0.77875 0.78243671]
----------------------------------------
Trial 2967
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=168,
random_state=100))])
cv score: [0.645 0.57875 0.73208333 0.78291667 0.79113924]
----------------------------------------
Trial 2968
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=34, random_state=100,
subsample=0.9))])
cv score: [0.77708333 0.65541667 0.77875 0.8575 0.83662975]
----------------------------------------
Trial 2969
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
n_estimators=12, random_state=100,
subsample=0.85))])
cv score: [0.71666667 0.65083333 0.72416667 0.83583333 0.69699367]
----------------------------------------
Trial 2970
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=141, random_state=100,
subsample=0.6))])
cv score: [0.66583333 0.58166667 0.67916667 0.78416667 0.81012658]
----------------------------------------
Trial 2971
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=170,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58291667 0.64666667 0.70666667 0.68166667 0.76344937]
----------------------------------------
Trial 2972
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
n_estimators=110, random_state=100,
subsample=0.95))])
cv score: [0.63083333 0.62333333 0.75583333 0.80583333 0.77373418]
----------------------------------------
Trial 2973
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=15, random_state=100,
subsample=0.7))])
cv score: [0.53916667 0.57583333 0.64583333 0.70583333 0.75712025]
----------------------------------------
Trial 2974
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=174, random_state=100,
subsample=0.95))])
cv score: [0.65416667 0.56166667 0.69583333 0.76583333 0.7903481 ]
----------------------------------------
Trial 2975
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=110, random_state=100))])
cv score: [0.55916667 0.72083333 0.695 0.78916667 0.79113924]
----------------------------------------
Trial 2976
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=127, random_state=100,
subsample=0.65))])
cv score: [0.69791667 0.60083333 0.67291667 0.66666667 0.52452532]
----------------------------------------
Trial 2977
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5525 0.67333333 0.75416667 0.75833333 0.77927215]
----------------------------------------
Trial 2978
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=96, random_state=100))])
cv score: [0.6675 0.66583333 0.68916667 0.82583333 0.86708861]
----------------------------------------
Trial 2979
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=57,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60166667 0.72 0.73583333 0.80833333 0.84335443]
----------------------------------------
Trial 2980
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='log2',
n_estimators=23, random_state=100,
subsample=0.9))])
cv score: [0.59833333 0.53416667 0.61583333 0.75416667 0.79113924]
----------------------------------------
Trial 2981
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=149, random_state=100))])
cv score: [0.72791667 0.6775 0.78041667 0.83541667 0.82792722]
----------------------------------------
Trial 2982
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='sqrt',
n_estimators=196, random_state=100,
subsample=0.6))])
cv score: [0.59416667 0.69583333 0.6925 0.79916667 0.82041139]
----------------------------------------
Trial 2983
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=99, random_state=100))])
cv score: [0.6825 0.64333333 0.725 0.8025 0.83860759]
----------------------------------------
Trial 2984
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=45, random_state=100,
subsample=0.65))])
cv score: [0.72666667 0.68416667 0.79333333 0.82 0.78401899]
----------------------------------------
Trial 2985
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=51, random_state=100))])
cv score: [0.7175 0.6725 0.775 0.84083333 0.84177215]
----------------------------------------
Trial 2986
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76 0.72416667 0.7725 0.85583333 0.91139241]
----------------------------------------
Trial 2987
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='sqrt',
n_estimators=182, random_state=100,
subsample=0.95))])
cv score: [0.60666667 0.54333333 0.66 0.765 0.73892405]
----------------------------------------
Trial 2988
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
n_estimators=146, random_state=100,
subsample=0.9))])
cv score: [0.64166667 0.59166667 0.745 0.77833333 0.75712025]
----------------------------------------
Trial 2989
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=37,
random_state=100))])
cv score: [0.66583333 0.59416667 0.69 0.79083333 0.82990506]
----------------------------------------
Trial 2990
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=70, random_state=100,
subsample=0.65))])
cv score: [0.67666667 0.5825 0.685 0.72666667 0.64477848]
----------------------------------------
Trial 2991
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=111,
random_state=100))])
cv score: [0.76458333 0.49375 0.78791667 0.70208333 0.79232595]
----------------------------------------
Trial 2992
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=87, random_state=100,
subsample=0.65))])
cv score: [0.64916667 0.59833333 0.70083333 0.7225 0.73417722]
----------------------------------------
Trial 2993
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
n_estimators=183, random_state=100,
subsample=0.8))])
cv score: [0.61666667 0.6725 0.7275 0.75416667 0.71518987]
----------------------------------------
Trial 2994
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64166667 0.6125 0.77416667 0.77166667 0.82911392]
----------------------------------------
Trial 2995
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=70, random_state=100,
subsample=0.95))])
cv score: [0.645 0.62833333 0.72833333 0.81166667 0.72389241]
----------------------------------------
Trial 2996
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.72416667 0.77083333 0.86583333 0.86550633]
----------------------------------------
Trial 2997
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=70,
random_state=100))])
cv score: [0.69166667 0.50125 0.72458333 0.64916667 0.63370253]
----------------------------------------
Trial 2998
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62416667 0.6425 0.775 0.75 0.7903481 ]
----------------------------------------
Trial 2999
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='log2',
n_estimators=103, random_state=100,
subsample=0.85))])
cv score: [0.6875 0.58833333 0.71583333 0.74583333 0.77768987]
----------------------------------------
Trial 3000
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=108,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 3001
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60666667 0.71875 0.70541667 0.81791667 0.78006329]
----------------------------------------
Trial 3002
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=42,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79666667 0.73583333 0.785 0.85333333 0.88291139]
----------------------------------------
Trial 3003
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=120, random_state=100,
subsample=0.7))])
cv score: [0.69916667 0.58166667 0.725 0.8025 0.83939873]
----------------------------------------
Trial 3004
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='sqrt',
n_estimators=134, random_state=100,
subsample=0.75))])
cv score: [0.68166667 0.60333333 0.745 0.81416667 0.78085443]
----------------------------------------
Trial 3005
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=113,
random_state=100))])
cv score: [0.78333333 0.595 0.78333333 0.78291667 0.76265823]
----------------------------------------
Trial 3006
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=128,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73 0.6925 0.80166667 0.84166667 0.90189873]
----------------------------------------
Trial 3007
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='sqrt',
n_estimators=10, random_state=100,
subsample=0.95))])
cv score: [0.63625 0.63666667 0.70916667 0.73041667 0.65031646]
----------------------------------------
Trial 3008
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=178,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6225 0.62166667 0.79166667 0.76166667 0.80933544]
----------------------------------------
Trial 3009
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5525 0.72958333 0.75708333 0.82083333 0.81408228]
----------------------------------------
Trial 3010
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=180,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 3011
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=22, random_state=100,
subsample=0.8))])
cv score: [0.665 0.66333333 0.72583333 0.78 0.81329114]
----------------------------------------
Trial 3012
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.72083333 0.77458333 0.86083333 0.86787975]
----------------------------------------
Trial 3013
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=198,
random_state=100))])
cv score: [0.68708333 0.49375 0.81 0.64791667 0.75316456]
----------------------------------------
Trial 3014
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=135,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61333333 0.6225 0.76333333 0.6975 0.67563291]
----------------------------------------
Trial 3015
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=169,
random_state=100, subsample=0.7))])
cv score: [0.70833333 0.7275 0.75333333 0.81333333 0.81487342]
----------------------------------------
Trial 3016
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=136, random_state=100,
subsample=0.6))])
cv score: [0.56166667 0.47666667 0.71 0.55583333 0.66455696]
----------------------------------------
Trial 3017
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='sqrt',
n_estimators=191, random_state=100,
subsample=0.8))])
cv score: [0.65166667 0.56916667 0.63 0.74166667 0.75712025]
----------------------------------------
Trial 3018
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=72, random_state=100))])
cv score: [0.72 0.66916667 0.77583333 0.825 0.84335443]
----------------------------------------
Trial 3019
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
n_estimators=164, random_state=100,
subsample=0.85))])
cv score: [0.655 0.60583333 0.74333333 0.78833333 0.71281646]
----------------------------------------
Trial 3020
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=171,
random_state=100))])
cv score: [0.68166667 0.62416667 0.6975 0.8075 0.83623418]
----------------------------------------
Trial 3021
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=97, random_state=100,
subsample=0.65))])
cv score: [0.68416667 0.685 0.72916667 0.79 0.83939873]
----------------------------------------
Trial 3022
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72416667 0.69333333 0.78583333 0.76166667 0.85126582]
----------------------------------------
Trial 3023
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=184,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.75083333 0.785 0.87 0.85126582]
----------------------------------------
Trial 3024
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=97,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66833333 0.60458333 0.76083333 0.7225 0.79509494]
----------------------------------------
Trial 3025
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=67,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.72875 0.75041667 0.84916667 0.85443038]
----------------------------------------
Trial 3026
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=101, random_state=100))])
cv score: [0.78166667 0.68333333 0.755 0.85333333 0.87025316]
----------------------------------------
Trial 3027
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=183,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77583333 0.7125 0.76583333 0.84666667 0.8931962 ]
----------------------------------------
Trial 3028
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=179,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72 0.695 0.79833333 0.835 0.8931962 ]
----------------------------------------
Trial 3029
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60416667 0.6375 0.75333333 0.755 0.79905063]
----------------------------------------
Trial 3030
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=183, random_state=100))])
cv score: [0.77666667 0.6925 0.77833333 0.85416667 0.8710443 ]
----------------------------------------
Trial 3031
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=35,
random_state=100))])
cv score: [0.68541667 0.49375 0.81083333 0.645 0.75237342]
----------------------------------------
Trial 3032
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=178,
random_state=100, subsample=0.6))])
cv score: [0.67083333 0.71166667 0.73 0.7825 0.83386076]
----------------------------------------
Trial 3033
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62583333 0.59 0.77916667 0.725 0.75237342]
----------------------------------------
Trial 3034
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=84,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7725 0.73416667 0.8125 0.84416667 0.87974684]
----------------------------------------
Trial 3035
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=67, random_state=100))])
cv score: [0.5825 0.71125 0.69166667 0.79333333 0.80696203]
----------------------------------------
Trial 3036
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=17,
random_state=100, subsample=0.6))])
cv score: [0.71541667 0.72791667 0.7525 0.80583333 0.88528481]
----------------------------------------
Trial 3037
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='sqrt',
n_estimators=155, random_state=100,
subsample=0.8))])
cv score: [0.71666667 0.61333333 0.70083333 0.81333333 0.80775316]
----------------------------------------
Trial 3038
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=54, random_state=100))])
cv score: [0.66 0.61458333 0.73625 0.81875 0.77571203]
----------------------------------------
Trial 3039
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt',
random_state=100))])
cv score: [0.61 0.67541667 0.69583333 0.81583333 0.82832278]
----------------------------------------
Trial 3040
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=69, random_state=100,
subsample=0.6))])
cv score: [0.76 0.645 0.70333333 0.835 0.77927215]
----------------------------------------
Trial 3041
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=157, random_state=100,
subsample=0.75))])
cv score: [0.65 0.5825 0.70583333 0.69833333 0.78639241]
----------------------------------------
Trial 3042
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=18, random_state=100))])
cv score: [0.7725 0.67 0.68791667 0.85541667 0.87262658]
----------------------------------------
Trial 3043
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=92,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.695 0.6825 0.79916667 0.8 0.8789557 ]
----------------------------------------
Trial 3044
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=193, random_state=100,
subsample=0.9))])
cv score: [0.56416667 0.56166667 0.66416667 0.67083333 0.70886076]
----------------------------------------
Trial 3045
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=193,
random_state=100))])
cv score: [0.67666667 0.58 0.72083333 0.81625 0.84414557]
----------------------------------------
Trial 3046
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=36, random_state=100,
subsample=0.7))])
cv score: [0.73333333 0.5975 0.68583333 0.67166667 0.65743671]
----------------------------------------
Trial 3047
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=127, random_state=100,
subsample=0.6))])
cv score: [0.49916667 0.46666667 0.62583333 0.56583333 0.70490506]
----------------------------------------
Trial 3048
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=173, random_state=100))])
cv score: [0.7775 0.69666667 0.7775 0.85083333 0.86946203]
----------------------------------------
Trial 3049
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=115,
random_state=100))])
cv score: [0.65583333 0.58916667 0.69833333 0.80875 0.81566456]
----------------------------------------
Trial 3050
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=27,
random_state=100))])
cv score: [0.76791667 0.49375 0.78958333 0.69791667 0.79232595]
----------------------------------------
Trial 3051
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=48,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65541667 0.72 0.66 0.83833333 0.84177215]
----------------------------------------
Trial 3052
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.6))])
cv score: [0.6625 0.62083333 0.6975 0.73166667 0.70411392]
----------------------------------------
Trial 3053
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=138,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59833333 0.5925 0.71333333 0.60958333 0.69224684]
----------------------------------------
Trial 3054
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=51,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78416667 0.71791667 0.78125 0.8425 0.87658228]
----------------------------------------
Trial 3055
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=22,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 3056
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=101,
random_state=100))])
cv score: [0.51375 0.66458333 0.71291667 0.82916667 0.76186709]
----------------------------------------
Trial 3057
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6725 0.67666667 0.76416667 0.76833333 0.86708861]
----------------------------------------
Trial 3058
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=144, random_state=100,
subsample=0.9))])
cv score: [0.55333333 0.64041667 0.67375 0.78958333 0.75435127]
----------------------------------------
Trial 3059
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=94, random_state=100,
subsample=0.75))])
cv score: [0.70083333 0.6225 0.68833333 0.82916667 0.80142405]
----------------------------------------
Trial 3060
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=19,
random_state=100))])
cv score: [0.75666667 0.64166667 0.6825 0.74833333 0.77927215]
----------------------------------------
Trial 3061
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=142, random_state=100,
subsample=0.95))])
cv score: [0.6875 0.62666667 0.655 0.7425 0.77531646]
----------------------------------------
Trial 3062
----------------------------------------
Parameters {'gb__n_estimators': 177, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=177,
random_state=100))])
cv score: [0.69 0.69083333 0.715 0.76333333 0.64240506]
----------------------------------------
Trial 3063
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=132,
random_state=100))])
cv score: [0.65583333 0.66916667 0.70083333 0.8175 0.84493671]
----------------------------------------
Trial 3064
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=52,
random_state=100))])
cv score: [0.68208333 0.45 0.74541667 0.6825 0.66772152]
----------------------------------------
Trial 3065
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=139, random_state=100,
subsample=0.85))])
cv score: [0.64916667 0.59833333 0.685 0.78666667 0.81012658]
----------------------------------------
Trial 3066
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=135, random_state=100,
subsample=0.7))])
cv score: [0.725 0.65916667 0.78 0.83916667 0.8306962 ]
----------------------------------------
Trial 3067
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72333333 0.7525 0.78833333 0.855 0.85363924]
----------------------------------------
Trial 3068
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72416667 0.7225 0.77291667 0.86833333 0.87025316]
----------------------------------------
Trial 3069
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=57,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.70916667 0.80416667 0.79083333 0.86392405]
----------------------------------------
Trial 3070
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67583333 0.63416667 0.75 0.73416667 0.79905063]
----------------------------------------
Trial 3071
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=30,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70666667 0.71916667 0.80583333 0.86 0.88053797]
----------------------------------------
Trial 3072
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=167, random_state=100,
subsample=0.7))])
cv score: [0.73333333 0.67583333 0.79416667 0.8325 0.8346519 ]
----------------------------------------
Trial 3073
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.75416667 0.78583333 0.87583333 0.85601266]
----------------------------------------
Trial 3074
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75 0.70916667 0.765 0.82166667 0.84810127]
----------------------------------------
Trial 3075
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.8))])
cv score: [0.69083333 0.65416667 0.695 0.84333333 0.84177215]
----------------------------------------
Trial 3076
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
n_estimators=129, random_state=100,
subsample=0.85))])
cv score: [0.72583333 0.6375 0.79583333 0.83 0.81408228]
----------------------------------------
Trial 3077
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.625 0.6475 0.74 0.67666667 0.79272152]
----------------------------------------
Trial 3078
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=106,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74375 0.72333333 0.75416667 0.865 0.89240506]
----------------------------------------
Trial 3079
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=77, random_state=100,
subsample=0.8))])
cv score: [0.71166667 0.63 0.75583333 0.825 0.80775316]
----------------------------------------
Trial 3080
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=45, random_state=100,
subsample=0.9))])
cv score: [0.65583333 0.61083333 0.66333333 0.75166667 0.79825949]
----------------------------------------
Trial 3081
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=131,
random_state=100))])
cv score: [0.62666667 0.67583333 0.7025 0.81333333 0.82911392]
----------------------------------------
Trial 3082
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=43,
random_state=100))])
cv score: [0.6725 0.62583333 0.65583333 0.73666667 0.78797468]
----------------------------------------
Trial 3083
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=199,
random_state=100))])
cv score: [0.5225 0.58666667 0.745 0.62 0.72389241]
----------------------------------------
Trial 3084
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=76, random_state=100,
subsample=0.9))])
cv score: [0.76416667 0.68625 0.68458333 0.84333333 0.83346519]
----------------------------------------
Trial 3085
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=61, random_state=100,
subsample=0.6))])
cv score: [0.5775 0.58416667 0.61333333 0.67333333 0.71044304]
----------------------------------------
Trial 3086
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=17,
random_state=100,
subsample=0.65))])
cv score: [0.63125 0.77625 0.68416667 0.86291667 0.88568038]
----------------------------------------
Trial 3087
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.565 0.6725 0.65041667 0.80208333 0.76542722]
----------------------------------------
Trial 3088
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76416667 0.73416667 0.78041667 0.84833333 0.86075949]
----------------------------------------
Trial 3089
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=160, random_state=100))])
cv score: [0.70083333 0.6325 0.735 0.79666667 0.81803797]
----------------------------------------
Trial 3090
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=156,
random_state=100))])
cv score: [0.68333333 0.59916667 0.7 0.79333333 0.81329114]
----------------------------------------
Trial 3091
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=59, random_state=100))])
cv score: [0.65916667 0.62708333 0.73125 0.83916667 0.78283228]
----------------------------------------
Trial 3092
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=120,
random_state=100))])
cv score: [0.69333333 0.61666667 0.71333333 0.80666667 0.82674051]
----------------------------------------
Trial 3093
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='log2',
n_estimators=28, random_state=100,
subsample=0.95))])
cv score: [0.6825 0.66916667 0.68916667 0.78416667 0.82674051]
----------------------------------------
Trial 3094
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68083333 0.74416667 0.77458333 0.86083333 0.85838608]
----------------------------------------
Trial 3095
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=134, random_state=100))])
cv score: [0.72291667 0.68 0.78 0.82666667 0.82318038]
----------------------------------------
Trial 3096
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=15, random_state=100))])
cv score: [0.69083333 0.62916667 0.70666667 0.75416667 0.78876582]
----------------------------------------
Trial 3097
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=146,
random_state=100))])
cv score: [0.685 0.6325 0.71666667 0.80583333 0.82594937]
----------------------------------------
Trial 3098
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=192, random_state=100))])
cv score: [0.755 0.6775 0.78416667 0.83833333 0.84731013]
----------------------------------------
Trial 3099
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
n_estimators=63, random_state=100,
subsample=0.75))])
cv score: [0.72166667 0.67625 0.80583333 0.83583333 0.81962025]
----------------------------------------
Trial 3100
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=12,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79416667 0.75125 0.79 0.84958333 0.88291139]
----------------------------------------
Trial 3101
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=57, random_state=100))])
cv score: [0.65458333 0.6075 0.70541667 0.80083333 0.81606013]
----------------------------------------
Trial 3102
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=193,
random_state=100))])
cv score: [0.54041667 0.66458333 0.71541667 0.82333333 0.79272152]
----------------------------------------
Trial 3103
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=119,
random_state=100))])
cv score: [0.65875 0.59791667 0.72125 0.78333333 0.76226266]
----------------------------------------
Trial 3104
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=173, random_state=100))])
cv score: [0.695 0.62916667 0.72083333 0.81666667 0.84256329]
----------------------------------------
Trial 3105
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=149, random_state=100))])
cv score: [0.73166667 0.67958333 0.77958333 0.83833333 0.83939873]
----------------------------------------
Trial 3106
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
n_estimators=193, random_state=100,
subsample=0.7))])
cv score: [0.7225 0.65083333 0.76833333 0.80166667 0.82199367]
----------------------------------------
Trial 3107
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.95))])
cv score: [0.725 0.61833333 0.69333333 0.82166667 0.84493671]
----------------------------------------
Trial 3108
----------------------------------------
Parameters {'rf__n_estimators': 125, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=125, random_state=100))])
cv score: [0.63875 0.69666667 0.65625 0.81416667 0.82120253]
----------------------------------------
Trial 3109
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66833333 0.6675 0.76416667 0.80083333 0.87816456]
----------------------------------------
Trial 3110
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=144,
random_state=100))])
cv score: [0.69333333 0.62666667 0.6875 0.79416667 0.82911392]
----------------------------------------
Trial 3111
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=149,
random_state=100))])
cv score: [0.72 0.59333333 0.7375 0.77916667 0.75474684]
----------------------------------------
Trial 3112
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=68, random_state=100))])
cv score: [0.7175 0.66 0.7325 0.79916667 0.87420886]
----------------------------------------
Trial 3113
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
n_estimators=94, random_state=100,
subsample=0.75))])
cv score: [0.72833333 0.64583333 0.81583333 0.83166667 0.8164557 ]
----------------------------------------
Trial 3114
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=184, random_state=100,
subsample=0.6))])
cv score: [0.60583333 0.5 0.70416667 0.675 0.71439873]
----------------------------------------
Trial 3115
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=12,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71083333 0.735 0.77958333 0.81916667 0.89596519]
----------------------------------------
Trial 3116
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=104, random_state=100,
subsample=0.85))])
cv score: [0.6475 0.59416667 0.70916667 0.80583333 0.78164557]
----------------------------------------
Trial 3117
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=138, random_state=100))])
cv score: [0.76666667 0.6975 0.7775 0.85 0.8789557 ]
----------------------------------------
Trial 3118
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
n_estimators=24, random_state=100,
subsample=0.7))])
cv score: [0.68333333 0.6875 0.805 0.82833333 0.84572785]
----------------------------------------
Trial 3119
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=148, random_state=100))])
cv score: [0.74166667 0.68125 0.78416667 0.835 0.84572785]
----------------------------------------
Trial 3120
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='sqrt',
n_estimators=52, random_state=100,
subsample=0.95))])
cv score: [0.63666667 0.545 0.70166667 0.79583333 0.79825949]
----------------------------------------
Trial 3121
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63833333 0.60166667 0.7575 0.7575 0.78639241]
----------------------------------------
Trial 3122
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=61,
random_state=100))])
cv score: [0.60916667 0.59666667 0.73916667 0.74625 0.80893987]
----------------------------------------
Trial 3123
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=82, random_state=100,
subsample=0.6))])
cv score: [0.77166667 0.63916667 0.67833333 0.785 0.78876582]
----------------------------------------
Trial 3124
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=19,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83916667 0.82199367]
----------------------------------------
Trial 3125
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=181,
random_state=100, subsample=0.6))])
cv score: [0.66916667 0.645 0.74666667 0.81 0.80696203]
----------------------------------------
Trial 3126
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=73,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75708333 0.71666667 0.73208333 0.86166667 0.85126582]
----------------------------------------
Trial 3127
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=21,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72458333 0.73333333 0.775 0.86041667 0.8477057 ]
----------------------------------------
Trial 3128
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76833333 0.71458333 0.81291667 0.8625 0.86550633]
----------------------------------------
Trial 3129
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=22,
random_state=100))])
cv score: [0.58083333 0.65916667 0.65708333 0.82958333 0.82871835]
----------------------------------------
Trial 3130
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=147,
random_state=100))])
cv score: [0.69416667 0.51791667 0.79916667 0.67 0.62856013]
----------------------------------------
Trial 3131
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=198,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7125 0.7 0.78 0.82 0.84968354]
----------------------------------------
Trial 3132
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
n_estimators=51, random_state=100,
subsample=0.7))])
cv score: [0.7225 0.65791667 0.785 0.80333333 0.8125 ]
----------------------------------------
Trial 3133
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=57, random_state=100))])
cv score: [0.6625 0.66916667 0.68666667 0.82166667 0.84968354]
----------------------------------------
Trial 3134
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=161,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60833333 0.57666667 0.77166667 0.71083333 0.81091772]
----------------------------------------
Trial 3135
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=141,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73166667 0.71958333 0.77458333 0.84166667 0.86946203]
----------------------------------------
Trial 3136
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=58,
random_state=100))])
cv score: [0.6125 0.60083333 0.73958333 0.74666667 0.81091772]
----------------------------------------
Trial 3137
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=11, random_state=100))])
cv score: [0.72833333 0.63041667 0.66583333 0.7325 0.79074367]
----------------------------------------
Trial 3138
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76833333 0.7375 0.78583333 0.86416667 0.90110759]
----------------------------------------
Trial 3139
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=140,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.6625 0.78876582]
----------------------------------------
Trial 3140
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=104,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79583333 0.72291667 0.80125 0.84416667 0.85126582]
----------------------------------------
Trial 3141
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.52791667 0.71125 0.68041667 0.80041667 0.73536392]
----------------------------------------
Trial 3142
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=151,
random_state=100))])
cv score: [0.67375 0.595 0.72375 0.79166667 0.7852057 ]
----------------------------------------
Trial 3143
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=89, random_state=100))])
cv score: [0.7 0.665 0.73 0.79416667 0.86629747]
----------------------------------------
Trial 3144
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, random_state=100))])
cv score: [0.68541667 0.49375 0.80916667 0.6475 0.75316456]
----------------------------------------
Trial 3145
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72708333 0.72291667 0.7525 0.85583333 0.89042722]
----------------------------------------
Trial 3146
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=51, random_state=100))])
cv score: [0.65333333 0.68083333 0.72333333 0.78833333 0.83227848]
----------------------------------------
Trial 3147
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=78,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83666667 0.82199367]
----------------------------------------
Trial 3148
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=179,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61083333 0.58333333 0.76666667 0.6975 0.75632911]
----------------------------------------
Trial 3149
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=192,
random_state=100))])
cv score: [0.54041667 0.66791667 0.71625 0.82166667 0.7903481 ]
----------------------------------------
Trial 3150
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=22, random_state=100,
subsample=0.7))])
cv score: [0.51083333 0.59666667 0.5875 0.59 0.77689873]
----------------------------------------
Trial 3151
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=172,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.54 0.57333333 0.66083333 0.71666667 0.74367089]
----------------------------------------
Trial 3152
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=87,
random_state=100))])
cv score: [0.7075 0.63708333 0.71166667 0.80833333 0.83702532]
----------------------------------------
Trial 3153
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76458333 0.74166667 0.76333333 0.83666667 0.85245253]
----------------------------------------
Trial 3154
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=53, random_state=100))])
cv score: [0.67333333 0.65833333 0.69 0.82166667 0.84731013]
----------------------------------------
Trial 3155
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='log2',
n_estimators=197,
random_state=100))])
cv score: [0.6625 0.64166667 0.7175 0.81083333 0.84098101]
----------------------------------------
Trial 3156
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=46, random_state=100))])
cv score: [0.61583333 0.68083333 0.70625 0.7975 0.84414557]
----------------------------------------
Trial 3157
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
n_estimators=112, random_state=100,
subsample=0.95))])
cv score: [0.58833333 0.65208333 0.61 0.78708333 0.74169304]
----------------------------------------
Trial 3158
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61583333 0.59916667 0.78416667 0.7625 0.70094937]
----------------------------------------
Trial 3159
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='sqrt',
n_estimators=141, random_state=100,
subsample=0.8))])
cv score: [0.62333333 0.6825 0.695 0.79166667 0.8085443 ]
----------------------------------------
Trial 3160
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6475 0.6975 0.75583333 0.795 0.87341772]
----------------------------------------
Trial 3161
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=131,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73166667 0.6875 0.77 0.81 0.90110759]
----------------------------------------
Trial 3162
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6375 0.62333333 0.6875 0.78666667 0.77294304]
----------------------------------------
Trial 3163
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62666667 0.66416667 0.69166667 0.74916667 0.80696203]
----------------------------------------
Trial 3164
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67 0.74083333 0.77375 0.81208333 0.84177215]
----------------------------------------
Trial 3165
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='sqrt',
n_estimators=90, random_state=100,
subsample=0.75))])
cv score: [0.69833333 0.63416667 0.67166667 0.7675 0.76107595]
----------------------------------------
Trial 3166
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='sqrt',
n_estimators=121, random_state=100,
subsample=0.9))])
cv score: [0.65416667 0.65666667 0.73666667 0.82416667 0.84968354]
----------------------------------------
Trial 3167
----------------------------------------
Parameters {'xgb__n_estimators': 63, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=63,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69583333 0.60666667 0.72666667 0.69583333 0.83781646]
----------------------------------------
Trial 3168
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=173,
random_state=100))])
cv score: [0.69083333 0.58 0.71583333 0.74916667 0.75079114]
----------------------------------------
Trial 3169
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72916667 0.72 0.76083333 0.86166667 0.8710443 ]
----------------------------------------
Trial 3170
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=174,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.68583333 0.79833333 0.83083333 0.88844937]
----------------------------------------
Trial 3171
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=99, random_state=100,
subsample=0.9))])
cv score: [0.59833333 0.59916667 0.69666667 0.785 0.58939873]
----------------------------------------
Trial 3172
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=129,
random_state=100))])
cv score: [0.6175 0.6775 0.70083333 0.815 0.82911392]
----------------------------------------
Trial 3173
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=63, random_state=100))])
cv score: [0.65916667 0.59916667 0.71666667 0.775 0.83860759]
----------------------------------------
Trial 3174
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=127,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 3175
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.76875 0.795 0.83125 0.875 ]
----------------------------------------
Trial 3176
----------------------------------------
Parameters {'xgb__n_estimators': 115, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=115,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67833333 0.67083333 0.7875 0.795 0.8528481 ]
----------------------------------------
Trial 3177
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=48, random_state=100))])
cv score: [0.67083333 0.625 0.73666667 0.79833333 0.85522152]
----------------------------------------
Trial 3178
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=134,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.6625 0.78876582]
----------------------------------------
Trial 3179
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=72, random_state=100,
subsample=0.7))])
cv score: [0.63666667 0.6 0.72666667 0.83 0.82278481]
----------------------------------------
Trial 3180
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69291667 0.75375 0.79625 0.86666667 0.8710443 ]
----------------------------------------
Trial 3181
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=162,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.69083333 0.78916667 0.80916667 0.88924051]
----------------------------------------
Trial 3182
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64583333 0.6475 0.76083333 0.75666667 0.8085443 ]
----------------------------------------
Trial 3183
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=55,
random_state=100))])
cv score: [0.60833333 0.59166667 0.73666667 0.74041667 0.8164557 ]
----------------------------------------
Trial 3184
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=39, random_state=100))])
cv score: [0.72833333 0.66333333 0.76666667 0.84333333 0.84098101]
----------------------------------------
Trial 3185
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
n_estimators=105, random_state=100,
subsample=0.7))])
cv score: [0.73083333 0.66916667 0.765 0.835 0.86075949]
----------------------------------------
Trial 3186
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.745 0.73041667 0.77666667 0.85416667 0.87737342]
----------------------------------------
Trial 3187
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=137,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.66 0.77666667 0.79 0.85917722]
----------------------------------------
Trial 3188
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.65))])
cv score: [0.6825 0.67 0.72833333 0.7925 0.84889241]
----------------------------------------
Trial 3189
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
n_estimators=57, random_state=100,
subsample=0.9))])
cv score: [0.75333333 0.63458333 0.78583333 0.835 0.79113924]
----------------------------------------
Trial 3190
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=191,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.51833333 0.58333333 0.755 0.675 0.72310127]
----------------------------------------
Trial 3191
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=150, random_state=100))])
cv score: [0.6925 0.63166667 0.7225 0.80666667 0.84810127]
----------------------------------------
Trial 3192
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=113, random_state=100,
subsample=0.75))])
cv score: [0.65583333 0.68 0.69083333 0.72 0.75870253]
----------------------------------------
Trial 3193
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=113,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.72916667 0.77166667 0.85666667 0.89240506]
----------------------------------------
Trial 3194
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=65, random_state=100))])
cv score: [0.75333333 0.67458333 0.77083333 0.82583333 0.83939873]
----------------------------------------
Trial 3195
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=41,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.745 0.70041667 0.78583333 0.83083333 0.87658228]
----------------------------------------
Trial 3196
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=36, random_state=100,
subsample=0.6))])
cv score: [0.65333333 0.53833333 0.7 0.58 0.82120253]
----------------------------------------
Trial 3197
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='log2',
n_estimators=157, random_state=100,
subsample=0.9))])
cv score: [0.67666667 0.63166667 0.69 0.79916667 0.83781646]
----------------------------------------
Trial 3198
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=157, random_state=100,
subsample=0.85))])
cv score: [0.66916667 0.57416667 0.68 0.81166667 0.81962025]
----------------------------------------
Trial 3199
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.8))])
cv score: [0.6425 0.58583333 0.69333333 0.74583333 0.78322785]
----------------------------------------
Trial 3200
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=167, random_state=100,
subsample=0.6))])
cv score: [0.68833333 0.66083333 0.69333333 0.81 0.84810127]
----------------------------------------
Trial 3201
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=191,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64166667 0.64666667 0.74333333 0.74083333 0.76186709]
----------------------------------------
Trial 3202
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='log2',
n_estimators=25, random_state=100,
subsample=0.75))])
cv score: [0.66583333 0.60666667 0.74583333 0.86833333 0.79825949]
----------------------------------------
Trial 3203
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='sqrt',
n_estimators=140, random_state=100,
subsample=0.65))])
cv score: [0.56333333 0.58833333 0.60833333 0.65666667 0.65348101]
----------------------------------------
Trial 3204
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='sqrt',
n_estimators=36, random_state=100,
subsample=0.65))])
cv score: [0.64833333 0.615 0.71833333 0.78666667 0.79984177]
----------------------------------------
Trial 3205
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=90, random_state=100,
subsample=0.7))])
cv score: [0.67583333 0.6325 0.7175 0.78166667 0.84018987]
----------------------------------------
Trial 3206
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57083333 0.50583333 0.67083333 0.74333333 0.79905063]
----------------------------------------
Trial 3207
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=82,
random_state=100))])
cv score: [0.6875 0.51375 0.82291667 0.6675 0.66495253]
----------------------------------------
Trial 3208
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='sqrt',
n_estimators=82, random_state=100,
subsample=0.8))])
cv score: [0.72333333 0.64083333 0.70416667 0.82916667 0.82832278]
----------------------------------------
Trial 3209
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='sqrt',
n_estimators=188, random_state=100,
subsample=0.65))])
cv score: [0.66666667 0.63416667 0.68083333 0.77416667 0.79984177]
----------------------------------------
Trial 3210
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=129,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79916667 0.66291667 0.78797468]
----------------------------------------
Trial 3211
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=150, random_state=100))])
cv score: [0.6775 0.62166667 0.73333333 0.80166667 0.83386076]
----------------------------------------
Trial 3212
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=132, random_state=100))])
cv score: [0.76833333 0.69583333 0.78666667 0.83583333 0.85522152]
----------------------------------------
Trial 3213
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=180, random_state=100,
subsample=0.95))])
cv score: [0.65333333 0.5975 0.6825 0.8025 0.83623418]
----------------------------------------
Trial 3214
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=184, random_state=100))])
cv score: [0.6625 0.645 0.73916667 0.80166667 0.84098101]
----------------------------------------
Trial 3215
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=29,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7825 0.7325 0.81416667 0.85416667 0.87262658]
----------------------------------------
Trial 3216
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=54, random_state=100,
subsample=0.9))])
cv score: [0.695 0.62666667 0.7575 0.7775 0.77056962]
----------------------------------------
Trial 3217
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=128,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68166667 0.6375 0.77666667 0.76083333 0.79509494]
----------------------------------------
Trial 3218
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=126, random_state=100))])
cv score: [0.64916667 0.67416667 0.74166667 0.7875 0.83623418]
----------------------------------------
Trial 3219
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=21, random_state=100))])
cv score: [0.66625 0.60125 0.69791667 0.68916667 0.82436709]
----------------------------------------
Trial 3220
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=94, random_state=100))])
cv score: [0.70583333 0.62 0.72416667 0.79583333 0.83623418]
----------------------------------------
Trial 3221
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=141,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6975 0.71791667 0.74416667 0.86083333 0.83148734]
----------------------------------------
Trial 3222
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='log2',
n_estimators=53, random_state=100,
subsample=0.65))])
cv score: [0.5875 0.6075 0.7375 0.5975 0.45332278]
----------------------------------------
Trial 3223
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=74, random_state=100))])
cv score: [0.665 0.63083333 0.75833333 0.81083333 0.81882911]
----------------------------------------
Trial 3224
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=64, random_state=100,
subsample=0.6))])
cv score: [0.69166667 0.695 0.71583333 0.81166667 0.79746835]
----------------------------------------
Trial 3225
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=62, random_state=100,
subsample=0.75))])
cv score: [0.705 0.63666667 0.68833333 0.78 0.77531646]
----------------------------------------
Trial 3226
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='log2',
n_estimators=38, random_state=100,
subsample=0.8))])
cv score: [0.61666667 0.64041667 0.68541667 0.84375 0.77136076]
----------------------------------------
Trial 3227
----------------------------------------
Parameters {'xgb__n_estimators': 26, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=26,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60833333 0.74166667 0.76583333 0.83583333 0.83939873]
----------------------------------------
Trial 3228
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.67166667 0.78583333 0.79916667 0.86867089]
----------------------------------------
Trial 3229
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.68333333 0.79166667 0.79 0.84572785]
----------------------------------------
Trial 3230
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=92,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77833333 0.7325 0.81083333 0.83833333 0.8789557 ]
----------------------------------------
Trial 3231
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=76,
random_state=100))])
cv score: [0.69166667 0.50125 0.72416667 0.64875 0.63330696]
----------------------------------------
Trial 3232
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='log2',
n_estimators=163, random_state=100,
subsample=0.95))])
cv score: [0.6825 0.57916667 0.70416667 0.78333333 0.82120253]
----------------------------------------
Trial 3233
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.6925 0.7725 0.8 0.8568038 ]
----------------------------------------
Trial 3234
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=124, random_state=100,
subsample=0.9))])
cv score: [0.715 0.665 0.82833333 0.84666667 0.80775316]
----------------------------------------
Trial 3235
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=42, random_state=100))])
cv score: [0.64291667 0.65166667 0.72708333 0.84291667 0.77650316]
----------------------------------------
Trial 3236
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=177,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72041667 0.71875 0.76708333 0.865 0.84612342]
----------------------------------------
Trial 3237
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='log2',
n_estimators=45, random_state=100,
subsample=0.6))])
cv score: [0.6875 0.7175 0.70166667 0.76666667 0.83860759]
----------------------------------------
Trial 3238
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=84,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.69833333 0.78416667 0.82083333 0.87025316]
----------------------------------------
Trial 3239
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=27, random_state=100))])
cv score: [0.68333333 0.58833333 0.7475 0.77166667 0.83227848]
----------------------------------------
Trial 3240
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=143,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53041667 0.6525 0.67041667 0.79583333 0.80102848]
----------------------------------------
Trial 3241
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=110,
random_state=100))])
cv score: [0.5775 0.66166667 0.70416667 0.83416667 0.83623418]
----------------------------------------
Trial 3242
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=113,
random_state=100))])
cv score: [0.69083333 0.63583333 0.69916667 0.80416667 0.82674051]
----------------------------------------
Trial 3243
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75333333 0.69916667 0.76416667 0.80916667 0.89873418]
----------------------------------------
Trial 3244
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='sqrt',
n_estimators=174, random_state=100,
subsample=0.9))])
cv score: [0.62166667 0.57 0.67583333 0.70833333 0.76028481]
----------------------------------------
Trial 3245
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=33,
random_state=100))])
cv score: [0.55 0.67166667 0.68041667 0.81291667 0.84137658]
----------------------------------------
Trial 3246
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=193, random_state=100))])
cv score: [0.73916667 0.68208333 0.72166667 0.85583333 0.88449367]
----------------------------------------
Trial 3247
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=13,
random_state=100))])
cv score: [0.63958333 0.67041667 0.71208333 0.82791667 0.85047468]
----------------------------------------
Trial 3248
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=182, random_state=100,
subsample=0.9))])
cv score: [0.75916667 0.6625 0.81875 0.83583333 0.78401899]
----------------------------------------
Trial 3249
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=57, random_state=100,
subsample=0.6))])
cv score: [0.66916667 0.5825 0.72166667 0.76333333 0.7943038 ]
----------------------------------------
Trial 3250
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=29,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79583333 0.71083333 0.80833333 0.83583333 0.87579114]
----------------------------------------
Trial 3251
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=34,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61583333 0.66083333 0.78333333 0.745 0.74920886]
----------------------------------------
Trial 3252
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=158, random_state=100,
subsample=0.9))])
cv score: [0.56666667 0.555 0.715 0.68666667 0.74446203]
----------------------------------------
Trial 3253
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=195,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.7525 0.78041667 0.8725 0.8710443 ]
----------------------------------------
Trial 3254
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=161,
random_state=100))])
cv score: [0.67916667 0.59583333 0.7025 0.7925 0.8125 ]
----------------------------------------
Trial 3255
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=63,
random_state=100))])
cv score: [0.62458333 0.595 0.71166667 0.78083333 0.78560127]
----------------------------------------
Trial 3256
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=158, random_state=100))])
cv score: [0.69791667 0.61 0.72833333 0.82208333 0.82120253]
----------------------------------------
Trial 3257
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71333333 0.69083333 0.77416667 0.82916667 0.89477848]
----------------------------------------
Trial 3258
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=11, random_state=100,
subsample=0.85))])
cv score: [0.5925 0.61916667 0.72416667 0.775 0.79351266]
----------------------------------------
Trial 3259
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=24,
random_state=100))])
cv score: [0.7975 0.6475 0.77416667 0.8325 0.86471519]
----------------------------------------
Trial 3260
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, n_estimators=144,
random_state=100,
subsample=0.85))])
cv score: [0.68666667 0.64666667 0.7375 0.79333333 0.81012658]
----------------------------------------
Trial 3261
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=105,
random_state=100))])
cv score: [0.67583333 0.6025 0.7175 0.80916667 0.81803797]
----------------------------------------
Trial 3262
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=20,
random_state=100))])
cv score: [0.72 0.60541667 0.68875 0.77833333 0.80379747]
----------------------------------------
Trial 3263
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=106,
random_state=100))])
cv score: [0.695 0.51791667 0.8 0.66958333 0.6289557 ]
----------------------------------------
Trial 3264
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.765 0.75166667 0.78416667 0.85416667 0.84968354]
----------------------------------------
Trial 3265
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=137,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74166667 0.67 0.77 0.77916667 0.89240506]
----------------------------------------
Trial 3266
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67416667 0.67083333 0.7825 0.79083333 0.86234177]
----------------------------------------
Trial 3267
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=82, random_state=100,
subsample=0.65))])
cv score: [0.69166667 0.64166667 0.69666667 0.755 0.77689873]
----------------------------------------
Trial 3268
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79 0.72875 0.6875 0.85416667 0.87618671]
----------------------------------------
Trial 3269
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7225 0.735 0.78166667 0.81833333 0.87816456]
----------------------------------------
Trial 3270
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74916667 0.72083333 0.75958333 0.86833333 0.86946203]
----------------------------------------
Trial 3271
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=96, random_state=100))])
cv score: [0.77 0.6875 0.7425 0.85833333 0.89556962]
----------------------------------------
Trial 3272
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='log2',
n_estimators=70, random_state=100,
subsample=0.95))])
cv score: [0.67833333 0.6575 0.72333333 0.82083333 0.82278481]
----------------------------------------
Trial 3273
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
n_estimators=24, random_state=100,
subsample=0.65))])
cv score: [0.74416667 0.66375 0.80083333 0.78833333 0.80696203]
----------------------------------------
Trial 3274
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=11,
random_state=100))])
cv score: [0.64333333 0.61291667 0.74541667 0.77 0.80577532]
----------------------------------------
Trial 3275
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=163,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78833333 0.74291667 0.78666667 0.86 0.85601266]
----------------------------------------
Trial 3276
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=36, random_state=100,
subsample=0.9))])
cv score: [0.6625 0.6075 0.67416667 0.81583333 0.75712025]
----------------------------------------
Trial 3277
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68083333 0.735 0.82 0.82333333 0.88212025]
----------------------------------------
Trial 3278
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=107, random_state=100))])
cv score: [0.61166667 0.70083333 0.69666667 0.7925 0.82357595]
----------------------------------------
Trial 3279
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72333333 0.705 0.7925 0.82083333 0.88924051]
----------------------------------------
Trial 3280
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=84,
random_state=100))])
cv score: [0.64916667 0.66833333 0.7 0.81166667 0.84098101]
----------------------------------------
Trial 3281
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
n_estimators=191, random_state=100,
subsample=0.85))])
cv score: [0.70166667 0.64083333 0.77833333 0.82 0.81012658]
----------------------------------------
Trial 3282
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71291667 0.72125 0.76208333 0.86333333 0.85007911]
----------------------------------------
Trial 3283
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=184, random_state=100))])
cv score: [0.66666667 0.665 0.70083333 0.8175 0.85601266]
----------------------------------------
Trial 3284
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=135,
random_state=100))])
cv score: [0.6925 0.4875 0.72 0.6675 0.63568038]
----------------------------------------
Trial 3285
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=13, random_state=100,
subsample=0.65))])
cv score: [0.5925 0.5875 0.7125 0.72 0.80221519]
----------------------------------------
Trial 3286
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78291667 0.74208333 0.76666667 0.84541667 0.89398734]
----------------------------------------
Trial 3287
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=134,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.665 0.6325 0.69916667 0.7 0.73971519]
----------------------------------------
Trial 3288
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.8))])
cv score: [0.60166667 0.40625 0.7125 0.51958333 0.48022152]
----------------------------------------
Trial 3289
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75 0.7325 0.78541667 0.83458333 0.87737342]
----------------------------------------
Trial 3290
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=67, random_state=100,
subsample=0.85))])
cv score: [0.68416667 0.63083333 0.71083333 0.775 0.8528481 ]
----------------------------------------
Trial 3291
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=94,
random_state=100, subsample=0.9))])
cv score: [0.71583333 0.64666667 0.8 0.82583333 0.79509494]
----------------------------------------
Trial 3292
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=96, random_state=100,
subsample=0.9))])
cv score: [0.57583333 0.615 0.7 0.7825 0.66455696]
----------------------------------------
Trial 3293
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=93, random_state=100,
subsample=0.6))])
cv score: [0.605 0.67333333 0.71083333 0.79416667 0.80300633]
----------------------------------------
Trial 3294
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=124, random_state=100,
subsample=0.85))])
cv score: [0.5825 0.57333333 0.65166667 0.65333333 0.75 ]
----------------------------------------
Trial 3295
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7275 0.7225 0.74 0.86541667 0.88449367]
----------------------------------------
Trial 3296
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=20,
random_state=100, subsample=0.9))])
cv score: [0.6175 0.58291667 0.78625 0.80416667 0.77966772]
----------------------------------------
Trial 3297
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=85,
random_state=100))])
cv score: [0.81333333 0.65083333 0.75 0.8325 0.84928797]
----------------------------------------
Trial 3298
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=180,
random_state=100))])
cv score: [0.71333333 0.6 0.67166667 0.81083333 0.84414557]
----------------------------------------
Trial 3299
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69166667 0.59833333 0.74083333 0.73583333 0.75079114]
----------------------------------------
Trial 3300
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5825 0.65666667 0.75166667 0.76 0.76898734]
----------------------------------------
Trial 3301
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76083333 0.72708333 0.77041667 0.85416667 0.85917722]
----------------------------------------
Trial 3302
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.80916667 0.74541667 0.81458333 0.8075 0.90506329]
----------------------------------------
Trial 3303
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75666667 0.7175 0.7775 0.84 0.87737342]
----------------------------------------
Trial 3304
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='sqrt',
n_estimators=77,
random_state=100))])
cv score: [0.65416667 0.59 0.7225 0.76833333 0.81012658]
----------------------------------------
Trial 3305
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.675 0.69083333 0.74083333 0.7675 0.81724684]
----------------------------------------
Trial 3306
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=24,
random_state=100))])
cv score: [0.7675 0.49375 0.79041667 0.69916667 0.79153481]
----------------------------------------
Trial 3307
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=56,
random_state=100))])
cv score: [0.68 0.59291667 0.7025 0.80375 0.84177215]
----------------------------------------
Trial 3308
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=175,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.69666667 0.80083333 0.8325 0.90743671]
----------------------------------------
Trial 3309
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.59416667 0.77833333 0.77166667 0.79113924]
----------------------------------------
Trial 3310
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=189, random_state=100,
subsample=0.9))])
cv score: [0.64 0.645 0.69416667 0.78583333 0.67246835]
----------------------------------------
Trial 3311
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=24,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.8375 0.74208861]
----------------------------------------
Trial 3312
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=20,
random_state=100))])
cv score: [0.7525 0.58791667 0.81083333 0.79375 0.80379747]
----------------------------------------
Trial 3313
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69583333 0.62833333 0.7675 0.66 0.78718354]
----------------------------------------
Trial 3314
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='log2',
n_estimators=172, random_state=100,
subsample=0.9))])
cv score: [0.61 0.52666667 0.63166667 0.74916667 0.75 ]
----------------------------------------
Trial 3315
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73 0.72083333 0.79416667 0.77416667 0.8528481 ]
----------------------------------------
Trial 3316
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=169,
random_state=100))])
cv score: [0.67041667 0.5875 0.71875 0.80166667 0.79588608]
----------------------------------------
Trial 3317
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=61, random_state=100,
subsample=0.8))])
cv score: [0.695 0.575 0.73 0.78333333 0.69541139]
----------------------------------------
Trial 3318
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=28, random_state=100,
subsample=0.65))])
cv score: [0.69583333 0.60333333 0.74583333 0.81583333 0.65189873]
----------------------------------------
Trial 3319
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=107,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74583333 0.71833333 0.795 0.81916667 0.82832278]
----------------------------------------
Trial 3320
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=50,
random_state=100))])
cv score: [0.7 0.65791667 0.70916667 0.775 0.84731013]
----------------------------------------
Trial 3321
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=151,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66833333 0.66166667 0.7825 0.79666667 0.86629747]
----------------------------------------
Trial 3322
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6125 0.70333333 0.70833333 0.84916667 0.83346519]
----------------------------------------
Trial 3323
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=31, random_state=100))])
cv score: [0.63 0.645 0.7175 0.7875 0.80537975]
----------------------------------------
Trial 3324
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=41,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77333333 0.75166667 0.8075 0.8625 0.88607595]
----------------------------------------
Trial 3325
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=123,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78166667 0.7325 0.7775 0.8625 0.87974684]
----------------------------------------
Trial 3326
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=183, random_state=100))])
cv score: [0.69333333 0.645 0.7075 0.8025 0.83781646]
----------------------------------------
Trial 3327
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=52, random_state=100,
subsample=0.6))])
cv score: [0.6825 0.68416667 0.71333333 0.79416667 0.76503165]
----------------------------------------
Trial 3328
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=65, random_state=100,
subsample=0.7))])
cv score: [0.63166667 0.62333333 0.6225 0.68 0.64003165]
----------------------------------------
Trial 3329
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=134,
random_state=100))])
cv score: [0.59541667 0.50083333 0.69666667 0.8425 0.67761076]
----------------------------------------
Trial 3330
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=136, random_state=100,
subsample=0.95))])
cv score: [0.73916667 0.6425 0.82 0.8075 0.80696203]
----------------------------------------
Trial 3331
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='sqrt',
n_estimators=107, random_state=100,
subsample=0.95))])
cv score: [0.53833333 0.65625 0.68041667 0.78708333 0.75237342]
----------------------------------------
Trial 3332
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=166, random_state=100))])
cv score: [0.70166667 0.61458333 0.73916667 0.82083333 0.79786392]
----------------------------------------
Trial 3333
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=195, random_state=100,
subsample=0.95))])
cv score: [0.73083333 0.62541667 0.78166667 0.8075 0.80063291]
----------------------------------------
Trial 3334
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=33,
random_state=100))])
cv score: [0.69041667 0.48916667 0.77958333 0.67125 0.63924051]
----------------------------------------
Trial 3335
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74583333 0.71791667 0.775 0.85583333 0.87579114]
----------------------------------------
Trial 3336
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=199, random_state=100))])
cv score: [0.6775 0.66083333 0.715 0.81333333 0.85601266]
----------------------------------------
Trial 3337
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=146, random_state=100,
subsample=0.9))])
cv score: [0.68 0.59083333 0.69083333 0.80083333 0.83860759]
----------------------------------------
Trial 3338
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=14, random_state=100))])
cv score: [0.7125 0.64916667 0.77125 0.86333333 0.81803797]
----------------------------------------
Trial 3339
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=123,
random_state=100))])
cv score: [0.63291667 0.57666667 0.83291667 0.70333333 0.78283228]
----------------------------------------
Trial 3340
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=27, random_state=100,
subsample=0.6))])
cv score: [0.63333333 0.71333333 0.7175 0.8475 0.80379747]
----------------------------------------
Trial 3341
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='log2',
n_estimators=115,
random_state=100))])
cv score: [0.68083333 0.5875 0.69083333 0.81 0.78718354]
----------------------------------------
Trial 3342
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=124,
random_state=100))])
cv score: [0.69291667 0.4875 0.72041667 0.66791667 0.63449367]
----------------------------------------
Trial 3343
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63 0.73916667 0.76375 0.8375 0.83662975]
----------------------------------------
Trial 3344
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=151, random_state=100,
subsample=0.7))])
cv score: [0.55166667 0.5075 0.72916667 0.7475 0.7056962 ]
----------------------------------------
Trial 3345
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=146,
random_state=100))])
cv score: [0.61 0.5925 0.71666667 0.72833333 0.75316456]
----------------------------------------
Trial 3346
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=107, random_state=100))])
cv score: [0.63958333 0.69916667 0.6425 0.82083333 0.82041139]
----------------------------------------
Trial 3347
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=138,
random_state=100))])
cv score: [0.6675 0.59166667 0.72208333 0.78708333 0.77650316]
----------------------------------------
Trial 3348
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=78, random_state=100))])
cv score: [0.70291667 0.63458333 0.73791667 0.8125 0.78599684]
----------------------------------------
Trial 3349
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63833333 0.72416667 0.75583333 0.8375 0.8528481 ]
----------------------------------------
Trial 3350
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=130, random_state=100,
subsample=0.8))])
cv score: [0.71833333 0.6075 0.70416667 0.79916667 0.80142405]
----------------------------------------
Trial 3351
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67833333 0.67 0.7725 0.80083333 0.84414557]
----------------------------------------
Trial 3352
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=182, random_state=100))])
cv score: [0.75583333 0.67833333 0.7825 0.835 0.85126582]
----------------------------------------
Trial 3353
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77833333 0.7325 0.7625 0.8525 0.87183544]
----------------------------------------
Trial 3354
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=142,
random_state=100))])
cv score: [0.6275 0.67666667 0.70333333 0.8175 0.82990506]
----------------------------------------
Trial 3355
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74583333 0.72583333 0.76958333 0.85333333 0.86550633]
----------------------------------------
Trial 3356
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.545 0.62833333 0.7525 0.69833333 0.77294304]
----------------------------------------
Trial 3357
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67416667 0.645 0.74333333 0.7475 0.82594937]
----------------------------------------
Trial 3358
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63416667 0.655 0.76416667 0.76916667 0.8085443 ]
----------------------------------------
Trial 3359
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=83, random_state=100,
subsample=0.7))])
cv score: [0.65166667 0.615 0.755 0.81416667 0.8346519 ]
----------------------------------------
Trial 3360
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=96,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77166667 0.74333333 0.80166667 0.86416667 0.86471519]
----------------------------------------
Trial 3361
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74416667 0.69083333 0.80666667 0.83583333 0.88844937]
----------------------------------------
Trial 3362
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=102, random_state=100))])
cv score: [0.60916667 0.69666667 0.70083333 0.79333333 0.8306962 ]
----------------------------------------
Trial 3363
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=89, random_state=100,
subsample=0.65))])
cv score: [0.71916667 0.73958333 0.75208333 0.865 0.85443038]
----------------------------------------
Trial 3364
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6 0.6625 0.795 0.755 0.78243671]
----------------------------------------
Trial 3365
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=132,
random_state=100))])
cv score: [0.67166667 0.6775 0.74708333 0.83416667 0.83860759]
----------------------------------------
Trial 3366
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=105, random_state=100))])
cv score: [0.69416667 0.63 0.72083333 0.80833333 0.83386076]
----------------------------------------
Trial 3367
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=114, random_state=100))])
cv score: [0.65166667 0.61833333 0.745 0.81 0.84414557]
----------------------------------------
Trial 3368
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=119, random_state=100))])
cv score: [0.6125 0.6975 0.70416667 0.79833333 0.82674051]
----------------------------------------
Trial 3369
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7375 0.71083333 0.77083333 0.83833333 0.93037975]
----------------------------------------
Trial 3370
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.69583333 0.79416667 0.83166667 0.88053797]
----------------------------------------
Trial 3371
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57416667 0.5775 0.72333333 0.73833333 0.81329114]
----------------------------------------
Trial 3372
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=50,
random_state=100,
subsample=0.85))])
cv score: [0.8125 0.61666667 0.74416667 0.7975 0.58227848]
----------------------------------------
Trial 3373
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=70,
random_state=100))])
cv score: [0.60583333 0.5875 0.715 0.68833333 0.76898734]
----------------------------------------
Trial 3374
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=117,
random_state=100))])
cv score: [0.635 0.5825 0.73875 0.78 0.78401899]
----------------------------------------
Trial 3375
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=174,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.66583333 0.75833333 0.78166667 0.84968354]
----------------------------------------
Trial 3376
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=79,
random_state=100))])
cv score: [0.73 0.59291667 0.77416667 0.7475 0.77294304]
----------------------------------------
Trial 3377
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=99, random_state=100,
subsample=0.95))])
cv score: [0.5525 0.58 0.65333333 0.68916667 0.70648734]
----------------------------------------
Trial 3378
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.695 0.77333333 0.8275 0.92958861]
----------------------------------------
Trial 3379
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=139,
random_state=100))])
cv score: [0.72333333 0.5925 0.73416667 0.77916667 0.74920886]
----------------------------------------
Trial 3380
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=59,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66333333 0.66833333 0.78 0.81333333 0.89003165]
----------------------------------------
Trial 3381
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=189,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70833333 0.71083333 0.74333333 0.86166667 0.86392405]
----------------------------------------
Trial 3382
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=199,
random_state=100))])
cv score: [0.54208333 0.66625 0.71625 0.82333333 0.79272152]
----------------------------------------
Trial 3383
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=172, random_state=100,
subsample=0.8))])
cv score: [0.66416667 0.59416667 0.70583333 0.805 0.84335443]
----------------------------------------
Trial 3384
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=40,
random_state=100))])
cv score: [0.69 0.4875 0.72041667 0.66916667 0.63528481]
----------------------------------------
Trial 3385
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.95))])
cv score: [0.58833333 0.30333333 0.67916667 0.72083333 0.71835443]
----------------------------------------
Trial 3386
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=57, random_state=100,
subsample=0.9))])
cv score: [0.62833333 0.57833333 0.69833333 0.75083333 0.77056962]
----------------------------------------
Trial 3387
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
n_estimators=154, random_state=100,
subsample=0.8))])
cv score: [0.7375 0.64666667 0.79083333 0.83833333 0.80063291]
----------------------------------------
Trial 3388
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5775 0.58833333 0.76333333 0.74166667 0.79905063]
----------------------------------------
Trial 3389
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.70416667 0.77333333 0.79416667 0.89636076]
----------------------------------------
Trial 3390
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=138,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.71833333 0.72458333 0.85666667 0.83148734]
----------------------------------------
Trial 3391
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=191, random_state=100))])
cv score: [0.57916667 0.70166667 0.70666667 0.8175 0.81724684]
----------------------------------------
Trial 3392
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=183, random_state=100))])
cv score: [0.75666667 0.6775 0.78333333 0.83333333 0.84968354]
----------------------------------------
Trial 3393
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=110, random_state=100,
subsample=0.7))])
cv score: [0.61583333 0.63333333 0.71166667 0.795 0.83623418]
----------------------------------------
Trial 3394
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61916667 0.60833333 0.73416667 0.66166667 0.8085443 ]
----------------------------------------
Trial 3395
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=106,
random_state=100))])
cv score: [0.68666667 0.665 0.71666667 0.80416667 0.84177215]
----------------------------------------
Trial 3396
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=29,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.67642405]
----------------------------------------
Trial 3397
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=167, random_state=100))])
cv score: [0.7 0.63166667 0.73083333 0.80333333 0.81882911]
----------------------------------------
Trial 3398
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=148, random_state=100))])
cv score: [0.66833333 0.66333333 0.73666667 0.8075 0.83860759]
----------------------------------------
Trial 3399
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=161, random_state=100))])
cv score: [0.67958333 0.63625 0.745 0.82083333 0.82080696]
----------------------------------------
Trial 3400
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.695 0.61833333 0.74666667 0.72666667 0.8346519 ]
----------------------------------------
Trial 3401
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=111, random_state=100))])
cv score: [0.65333333 0.62083333 0.745 0.81 0.84889241]
----------------------------------------
Trial 3402
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7575 0.7075 0.77666667 0.81583333 0.88291139]
----------------------------------------
Trial 3403
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=19,
random_state=100))])
cv score: [0.73125 0.59208333 0.68041667 0.75666667 0.80933544]
----------------------------------------
Trial 3404
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=182, random_state=100))])
cv score: [0.69166667 0.62166667 0.73916667 0.8175 0.82238924]
----------------------------------------
Trial 3405
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.73666667 0.76041667 0.83208333 0.8568038 ]
----------------------------------------
Trial 3406
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59083333 0.5975 0.75583333 0.78 0.74841772]
----------------------------------------
Trial 3407
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=41, random_state=100))])
cv score: [0.76583333 0.68625 0.75583333 0.845 0.89082278]
----------------------------------------
Trial 3408
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=16,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61208333 0.74583333 0.70875 0.7975 0.74683544]
----------------------------------------
Trial 3409
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69833333 0.68166667 0.78083333 0.79833333 0.88370253]
----------------------------------------
Trial 3410
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=99,
random_state=100))])
cv score: [0.64 0.58916667 0.73625 0.77958333 0.78322785]
----------------------------------------
Trial 3411
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58083333 0.57833333 0.80166667 0.68666667 0.72151899]
----------------------------------------
Trial 3412
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72333333 0.68833333 0.78416667 0.8 0.87420886]
----------------------------------------
Trial 3413
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73708333 0.73708333 0.78583333 0.84875 0.90585443]
----------------------------------------
Trial 3414
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=186, random_state=100))])
cv score: [0.72291667 0.67708333 0.77875 0.8375 0.8306962 ]
----------------------------------------
Trial 3415
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=34, random_state=100,
subsample=0.75))])
cv score: [0.67833333 0.65416667 0.67416667 0.82 0.77056962]
----------------------------------------
Trial 3416
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.69 0.64166667 0.7225 0.8125 0.83386076]
----------------------------------------
Trial 3417
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=113, random_state=100))])
cv score: [0.65333333 0.65416667 0.69666667 0.82833333 0.85522152]
----------------------------------------
Trial 3418
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=78, random_state=100))])
cv score: [0.755 0.69166667 0.74583333 0.85416667 0.88291139]
----------------------------------------
Trial 3419
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7275 0.72958333 0.765 0.84666667 0.89398734]
----------------------------------------
Trial 3420
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='sqrt',
n_estimators=199, random_state=100,
subsample=0.6))])
cv score: [0.66333333 0.64833333 0.6925 0.80833333 0.84335443]
----------------------------------------
Trial 3421
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64875 0.71916667 0.71083333 0.85083333 0.83702532]
----------------------------------------
Trial 3422
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.67416667 0.79 0.8 0.87262658]
----------------------------------------
Trial 3423
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=40,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69 0.74708333 0.7725 0.83458333 0.87302215]
----------------------------------------
Trial 3424
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=21,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.65583333 0.73583333 0.75 0.83781646]
----------------------------------------
Trial 3425
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=75,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.80666667 0.73041667 0.8075 0.84541667 0.8568038 ]
----------------------------------------
Trial 3426
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=32,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.795 0.71916667 0.79666667 0.83916667 0.89556962]
----------------------------------------
Trial 3427
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.71666667 0.80083333 0.81916667 0.89161392]
----------------------------------------
Trial 3428
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.8))])
cv score: [0.59083333 0.59833333 0.67083333 0.6975 0.77927215]
----------------------------------------
Trial 3429
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=11, random_state=100,
subsample=0.9))])
cv score: [0.68666667 0.6125 0.76166667 0.73 0.72547468]
----------------------------------------
Trial 3430
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
n_estimators=147, random_state=100,
subsample=0.8))])
cv score: [0.77583333 0.67666667 0.77458333 0.8475 0.85996835]
----------------------------------------
Trial 3431
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='sqrt',
n_estimators=70,
random_state=100))])
cv score: [0.61333333 0.57916667 0.70833333 0.78 0.77768987]
----------------------------------------
Trial 3432
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=90,
random_state=100))])
cv score: [0.63875 0.59666667 0.73208333 0.77875 0.78362342]
----------------------------------------
Trial 3433
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=62,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63416667 0.6575 0.7275 0.73333333 0.75791139]
----------------------------------------
Trial 3434
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=113,
random_state=100))])
cv score: [0.56375 0.65708333 0.69083333 0.83291667 0.80102848]
----------------------------------------
Trial 3435
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=112,
random_state=100))])
cv score: [0.57416667 0.66583333 0.70583333 0.83083333 0.83306962]
----------------------------------------
Trial 3436
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71583333 0.73708333 0.75875 0.83666667 0.85759494]
----------------------------------------
Trial 3437
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=147, random_state=100,
subsample=0.7))])
cv score: [0.56333333 0.59583333 0.69416667 0.645 0.74841772]
----------------------------------------
Trial 3438
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
n_estimators=139, random_state=100,
subsample=0.65))])
cv score: [0.6925 0.67166667 0.75583333 0.825 0.82515823]
----------------------------------------
Trial 3439
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75666667 0.66416667 0.77791667 0.8225 0.86867089]
----------------------------------------
Trial 3440
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6875 0.6675 0.75916667 0.74166667 0.81724684]
----------------------------------------
Trial 3441
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62833333 0.6075 0.805 0.69333333 0.81170886]
----------------------------------------
Trial 3442
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='log2',
n_estimators=173, random_state=100,
subsample=0.95))])
cv score: [0.7025 0.63833333 0.71083333 0.81833333 0.83939873]
----------------------------------------
Trial 3443
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73666667 0.65 0.77583333 0.755 0.78797468]
----------------------------------------
Trial 3444
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=195, random_state=100))])
cv score: [0.665 0.65 0.73666667 0.80583333 0.85126582]
----------------------------------------
Trial 3445
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=69, random_state=100))])
cv score: [0.65 0.65333333 0.705 0.82666667 0.8528481 ]
----------------------------------------
Trial 3446
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
n_estimators=39, random_state=100,
subsample=0.85))])
cv score: [0.6775 0.58 0.70166667 0.7925 0.73655063]
----------------------------------------
Trial 3447
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=56,
random_state=100))])
cv score: [0.645 0.67291667 0.6775 0.79916667 0.83386076]
----------------------------------------
Trial 3448
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67333333 0.65083333 0.7925 0.76833333 0.84731013]
----------------------------------------
Trial 3449
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=48,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66833333 0.75458333 0.75125 0.865 0.82199367]
----------------------------------------
Trial 3450
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=183,
random_state=100))])
cv score: [0.56625 0.66208333 0.70833333 0.82125 0.81408228]
----------------------------------------
Trial 3451
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=194, random_state=100,
subsample=0.6))])
cv score: [0.38 0.59583333 0.41875 0.47166667 0.43868671]
----------------------------------------
Trial 3452
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=87,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78666667 0.73333333 0.79 0.865 0.86946203]
----------------------------------------
Trial 3453
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=163, random_state=100))])
cv score: [0.65666667 0.655 0.70416667 0.82 0.84968354]
----------------------------------------
Trial 3454
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=198,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6325 0.64083333 0.77166667 0.775 0.87183544]
----------------------------------------
Trial 3455
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=77, random_state=100))])
cv score: [0.69416667 0.61166667 0.72083333 0.78416667 0.83544304]
----------------------------------------
Trial 3456
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=70,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70875 0.72125 0.73083333 0.85625 0.87143987]
----------------------------------------
Trial 3457
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.65))])
cv score: [0.53416667 0.655 0.67458333 0.79083333 0.77452532]
----------------------------------------
Trial 3458
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=178, random_state=100))])
cv score: [0.69166667 0.6225 0.74166667 0.8225 0.82318038]
----------------------------------------
Trial 3459
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5825 0.64083333 0.76833333 0.73833333 0.83544304]
----------------------------------------
Trial 3460
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=39,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71916667 0.71083333 0.78833333 0.85333333 0.89161392]
----------------------------------------
Trial 3461
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=90,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.75583333 0.77708333 0.84041667 0.87579114]
----------------------------------------
Trial 3462
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=37,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 3463
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=151, random_state=100))])
cv score: [0.69166667 0.61166667 0.73333333 0.825 0.82832278]
----------------------------------------
Trial 3464
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76166667 0.72125 0.79583333 0.82666667 0.89477848]
----------------------------------------
Trial 3465
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=13, random_state=100,
subsample=0.6))])
cv score: [0.67916667 0.62833333 0.68916667 0.73583333 0.85917722]
----------------------------------------
Trial 3466
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=53, random_state=100))])
cv score: [0.75583333 0.7 0.7475 0.85333333 0.88686709]
----------------------------------------
Trial 3467
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.6775 0.79833333 0.81666667 0.87025316]
----------------------------------------
Trial 3468
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=22, random_state=100))])
cv score: [0.60916667 0.6625 0.69125 0.78916667 0.84889241]
----------------------------------------
Trial 3469
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=128,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.65916667 0.76916667 0.795 0.86946203]
----------------------------------------
Trial 3470
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=181, random_state=100,
subsample=0.6))])
cv score: [0.63916667 0.58 0.65166667 0.6925 0.71598101]
----------------------------------------
Trial 3471
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=82,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76666667 0.7425 0.79166667 0.81916667 0.84731013]
----------------------------------------
Trial 3472
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=103, random_state=100))])
cv score: [0.6925 0.64416667 0.7325 0.81166667 0.8306962 ]
----------------------------------------
Trial 3473
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='sqrt',
n_estimators=41, random_state=100,
subsample=0.85))])
cv score: [0.62833333 0.5725 0.72916667 0.73666667 0.78401899]
----------------------------------------
Trial 3474
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.62916667 0.69083333 0.70666667 0.80166667 0.83306962]
----------------------------------------
Trial 3475
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.765 0.71833333 0.78083333 0.84083333 0.86787975]
----------------------------------------
Trial 3476
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=119, random_state=100,
subsample=0.6))])
cv score: [0.64166667 0.5675 0.70083333 0.73416667 0.79825949]
----------------------------------------
Trial 3477
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66416667 0.56291667 0.69 0.69083333 0.75949367]
----------------------------------------
Trial 3478
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=123, random_state=100,
subsample=0.6))])
cv score: [0.71166667 0.64416667 0.64583333 0.745 0.78797468]
----------------------------------------
Trial 3479
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=151,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 3480
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=118, random_state=100))])
cv score: [0.72083333 0.67541667 0.78375 0.83333333 0.83346519]
----------------------------------------
Trial 3481
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=131,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72916667 0.72875 0.77208333 0.8575 0.84810127]
----------------------------------------
Trial 3482
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=29, random_state=100))])
cv score: [0.71791667 0.6575 0.77208333 0.85125 0.83900316]
----------------------------------------
Trial 3483
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=158,
random_state=100))])
cv score: [0.64 0.55333333 0.7075 0.73833333 0.74841772]
----------------------------------------
Trial 3484
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=174,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63333333 0.6075 0.7525 0.74416667 0.82199367]
----------------------------------------
Trial 3485
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.66916667 0.76083333 0.75916667 0.81091772]
----------------------------------------
Trial 3486
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74083333 0.72333333 0.78041667 0.83833333 0.86550633]
----------------------------------------
Trial 3487
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=125,
random_state=100))])
cv score: [0.63 0.58583333 0.74 0.7225 0.7278481 ]
----------------------------------------
Trial 3488
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=43,
random_state=100))])
cv score: [0.64666667 0.57916667 0.68666667 0.78916667 0.83623418]
----------------------------------------
Trial 3489
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=54,
random_state=100))])
cv score: [0.6775 0.60125 0.7 0.80583333 0.84572785]
----------------------------------------
Trial 3490
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=133, random_state=100))])
cv score: [0.74833333 0.68125 0.78833333 0.83166667 0.84731013]
----------------------------------------
Trial 3491
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=11,
random_state=100))])
cv score: [0.74833333 0.63416667 0.675 0.73041667 0.69897152]
----------------------------------------
Trial 3492
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=30, random_state=100,
subsample=0.75))])
cv score: [0.54083333 0.62666667 0.7475 0.64833333 0.78718354]
----------------------------------------
Trial 3493
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=14, random_state=100,
subsample=0.6))])
cv score: [0.67333333 0.65 0.66166667 0.805 0.77056962]
----------------------------------------
Trial 3494
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=87,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72166667 0.64916667 0.77833333 0.76583333 0.82199367]
----------------------------------------
Trial 3495
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=26, random_state=100))])
cv score: [0.73375 0.68166667 0.77833333 0.83583333 0.82911392]
----------------------------------------
Trial 3496
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=158,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58 0.6075 0.72333333 0.71666667 0.78797468]
----------------------------------------
Trial 3497
----------------------------------------
Parameters {'xgb__n_estimators': 95, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=95,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76166667 0.72416667 0.78166667 0.85166667 0.91455696]
----------------------------------------
Trial 3498
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=58,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71083333 0.715 0.8025 0.845 0.87420886]
----------------------------------------
Trial 3499
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='log2',
n_estimators=12, random_state=100,
subsample=0.7))])
cv score: [0.68833333 0.6025 0.61 0.66583333 0.69699367]
----------------------------------------
Trial 3500
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71666667 0.57666667 0.75833333 0.80916667 0.72863924]
----------------------------------------
Trial 3501
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=161, random_state=100,
subsample=0.85))])
cv score: [0.63083333 0.57166667 0.66833333 0.795 0.79984177]
----------------------------------------
Trial 3502
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=12,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59583333 0.72166667 0.76916667 0.77375 0.85324367]
----------------------------------------
Trial 3503
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71916667 0.72958333 0.76375 0.85875 0.86155063]
----------------------------------------
Trial 3504
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='sqrt',
n_estimators=51, random_state=100,
subsample=0.65))])
cv score: [0.66 0.69291667 0.6725 0.79416667 0.81962025]
----------------------------------------
Trial 3505
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=122,
random_state=100))])
cv score: [0.61166667 0.67333333 0.69416667 0.81833333 0.82990506]
----------------------------------------
Trial 3506
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=146,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84458333 0.74208861]
----------------------------------------
Trial 3507
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=120,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 3508
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=150,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72083333 0.7075 0.7925 0.8225 0.85601266]
----------------------------------------
Trial 3509
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=154, random_state=100,
subsample=0.8))])
cv score: [0.46833333 0.59083333 0.62916667 0.735 0.75553797]
----------------------------------------
Trial 3510
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=122, random_state=100))])
cv score: [0.6125 0.6975 0.7025 0.80166667 0.82674051]
----------------------------------------
Trial 3511
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=32,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.68375 0.7575 0.83166667 0.88370253]
----------------------------------------
Trial 3512
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=54, random_state=100))])
cv score: [0.62083333 0.66166667 0.72 0.7825 0.81962025]
----------------------------------------
Trial 3513
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=154, random_state=100,
subsample=0.85))])
cv score: [0.6975 0.6175 0.69333333 0.79333333 0.83939873]
----------------------------------------
Trial 3514
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=195, random_state=100,
subsample=0.6))])
cv score: [0.62416667 0.56083333 0.64833333 0.67833333 0.70253165]
----------------------------------------
Trial 3515
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62333333 0.64166667 0.745 0.79083333 0.80063291]
----------------------------------------
Trial 3516
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.72416667 0.77583333 0.85166667 0.86313291]
----------------------------------------
Trial 3517
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
n_estimators=26, random_state=100,
subsample=0.6))])
cv score: [0.6975 0.68041667 0.77666667 0.83 0.78481013]
----------------------------------------
Trial 3518
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=136, random_state=100,
subsample=0.95))])
cv score: [0.66666667 0.53833333 0.70416667 0.7775 0.73971519]
----------------------------------------
Trial 3519
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=117,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.72208333 0.76458333 0.8475 0.86471519]
----------------------------------------
Trial 3520
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=114,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74916667 0.73333333 0.7775 0.82333333 0.91297468]
----------------------------------------
Trial 3521
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70333333 0.70916667 0.79666667 0.82 0.89952532]
----------------------------------------
Trial 3522
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=97,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63416667 0.69166667 0.76583333 0.76166667 0.79272152]
----------------------------------------
Trial 3523
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=102, random_state=100,
subsample=0.95))])
cv score: [0.68666667 0.5875 0.73 0.81 0.81487342]
----------------------------------------
Trial 3524
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='log2',
n_estimators=35, random_state=100,
subsample=0.8))])
cv score: [0.48666667 0.6 0.70333333 0.56 0.78718354]
----------------------------------------
Trial 3525
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69166667 0.75083333 0.76875 0.86 0.85363924]
----------------------------------------
Trial 3526
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=126,
random_state=100))])
cv score: [0.68666667 0.51375 0.82458333 0.66375 0.66495253]
----------------------------------------
Trial 3527
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=164, random_state=100))])
cv score: [0.67833333 0.61583333 0.73916667 0.79833333 0.83227848]
----------------------------------------
Trial 3528
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=173,
random_state=100))])
cv score: [0.68708333 0.51375 0.82291667 0.66375 0.66416139]
----------------------------------------
Trial 3529
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=111, random_state=100))])
cv score: [0.70583333 0.60083333 0.73666667 0.83083333 0.82001582]
----------------------------------------
Trial 3530
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
n_estimators=66, random_state=100,
subsample=0.6))])
cv score: [0.71583333 0.69416667 0.785 0.83166667 0.82515823]
----------------------------------------
Trial 3531
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='sqrt',
n_estimators=73, random_state=100,
subsample=0.6))])
cv score: [0.39333333 0.58333333 0.58333333 0.7175 0.57041139]
----------------------------------------
Trial 3532
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=109, random_state=100,
subsample=0.7))])
cv score: [0.67666667 0.62083333 0.73916667 0.805 0.84414557]
----------------------------------------
Trial 3533
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=149, random_state=100,
subsample=0.65))])
cv score: [0.73666667 0.65333333 0.77916667 0.83583333 0.82990506]
----------------------------------------
Trial 3534
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=137, random_state=100,
subsample=0.6))])
cv score: [0.48291667 0.585 0.57166667 0.51833333 0.58227848]
----------------------------------------
Trial 3535
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77 0.72916667 0.7975 0.83083333 0.88291139]
----------------------------------------
Trial 3536
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.74083333 0.77541667 0.83916667 0.86867089]
----------------------------------------
Trial 3537
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=120, random_state=100))])
cv score: [0.71791667 0.61041667 0.75166667 0.81333333 0.79825949]
----------------------------------------
Trial 3538
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=52,
random_state=100))])
cv score: [0.6975 0.65291667 0.70833333 0.7825 0.84968354]
----------------------------------------
Trial 3539
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=142,
random_state=100))])
cv score: [0.76458333 0.49375 0.78708333 0.70208333 0.79232595]
----------------------------------------
Trial 3540
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=32,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65333333 0.71916667 0.72666667 0.88458333 0.84137658]
----------------------------------------
Trial 3541
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.50666667 0.60916667 0.66416667 0.8025 0.74050633]
----------------------------------------
Trial 3542
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
n_estimators=140, random_state=100,
subsample=0.9))])
cv score: [0.74833333 0.61666667 0.82625 0.83916667 0.77927215]
----------------------------------------
Trial 3543
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='sqrt',
random_state=100))])
cv score: [0.67833333 0.645 0.72 0.79333333 0.83702532]
----------------------------------------
Trial 3544
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.795 0.73041667 0.79375 0.84083333 0.86234177]
----------------------------------------
Trial 3545
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=69,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72333333 0.74541667 0.79208333 0.86833333 0.85996835]
----------------------------------------
Trial 3546
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
n_estimators=142,
random_state=100))])
cv score: [0.59666667 0.59 0.78125 0.72666667 0.66851266]
----------------------------------------
Trial 3547
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=34, random_state=100))])
cv score: [0.67583333 0.61083333 0.74583333 0.81541667 0.84256329]
----------------------------------------
Trial 3548
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
n_estimators=21, random_state=100,
subsample=0.85))])
cv score: [0.69833333 0.62625 0.78833333 0.87166667 0.76186709]
----------------------------------------
Trial 3549
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='sqrt',
n_estimators=168, random_state=100,
subsample=0.85))])
cv score: [0.56916667 0.645 0.64083333 0.6925 0.75553797]
----------------------------------------
Trial 3550
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=39, random_state=100,
subsample=0.95))])
cv score: [0.70333333 0.58916667 0.77041667 0.83416667 0.77452532]
----------------------------------------
Trial 3551
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=22, random_state=100))])
cv score: [0.70416667 0.645 0.67 0.805 0.84651899]
----------------------------------------
Trial 3552
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53166667 0.66416667 0.68 0.67083333 0.80300633]
----------------------------------------
Trial 3553
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=69, random_state=100))])
cv score: [0.67916667 0.62291667 0.7325 0.815 0.78876582]
----------------------------------------
Trial 3554
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='sqrt',
n_estimators=97, random_state=100,
subsample=0.9))])
cv score: [0.575 0.58083333 0.6325 0.70666667 0.69066456]
----------------------------------------
Trial 3555
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74083333 0.71666667 0.75416667 0.86 0.87025316]
----------------------------------------
Trial 3556
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='log2',
n_estimators=132,
random_state=100))])
cv score: [0.515 0.55583333 0.69166667 0.62083333 0.72151899]
----------------------------------------
Trial 3557
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=75, random_state=100,
subsample=0.95))])
cv score: [0.59916667 0.3525 0.68833333 0.725 0.72231013]
----------------------------------------
Trial 3558
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=49,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78166667 0.73625 0.7775 0.83916667 0.88686709]
----------------------------------------
Trial 3559
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=114,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7125 0.7 0.78916667 0.78416667 0.86708861]
----------------------------------------
Trial 3560
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=128, random_state=100))])
cv score: [0.68916667 0.5975 0.74166667 0.82125 0.82515823]
----------------------------------------
Trial 3561
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=75, random_state=100))])
cv score: [0.69333333 0.6725 0.72916667 0.79083333 0.86471519]
----------------------------------------
Trial 3562
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, n_estimators=19,
random_state=100,
subsample=0.95))])
cv score: [0.74666667 0.6575 0.71208333 0.84666667 0.85403481]
----------------------------------------
Trial 3563
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(n_estimators=136, random_state=100,
subsample=0.8))])
cv score: [0.71 0.66666667 0.76583333 0.78333333 0.82199367]
----------------------------------------
Trial 3564
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.75))])
cv score: [0.5775 0.6275 0.60083333 0.61916667 0.69620253]
----------------------------------------
Trial 3565
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59916667 0.66083333 0.77833333 0.77583333 0.83306962]
----------------------------------------
Trial 3566
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=194, random_state=100))])
cv score: [0.665 0.65 0.7375 0.8075 0.84889241]
----------------------------------------
Trial 3567
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
n_estimators=95, random_state=100,
subsample=0.65))])
cv score: [0.72 0.67833333 0.77083333 0.81583333 0.82199367]
----------------------------------------
Trial 3568
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=196,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79916667 0.66416667 0.78876582]
----------------------------------------
Trial 3569
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=185,
random_state=100))])
cv score: [0.65583333 0.66333333 0.69583333 0.82666667 0.84414557]
----------------------------------------
Trial 3570
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='log2',
n_estimators=133, random_state=100,
subsample=0.75))])
cv score: [0.68083333 0.63416667 0.71833333 0.83333333 0.82357595]
----------------------------------------
Trial 3571
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=134,
random_state=100))])
cv score: [0.68666667 0.58666667 0.69833333 0.79416667 0.80300633]
----------------------------------------
Trial 3572
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=123,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7025 0.68083333 0.7875 0.78666667 0.87737342]
----------------------------------------
Trial 3573
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=131,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69583333 0.6825 0.785 0.79166667 0.81803797]
----------------------------------------
Trial 3574
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=17, random_state=100))])
cv score: [0.70791667 0.66375 0.70458333 0.87625 0.86708861]
----------------------------------------
Trial 3575
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=94, random_state=100,
subsample=0.9))])
cv score: [0.72 0.62 0.825 0.84833333 0.77610759]
----------------------------------------
Trial 3576
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=95, random_state=100,
subsample=0.95))])
cv score: [0.65583333 0.58583333 0.72083333 0.76666667 0.79905063]
----------------------------------------
Trial 3577
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=99,
random_state=100))])
cv score: [0.75 0.58791667 0.80916667 0.79041667 0.80458861]
----------------------------------------
Trial 3578
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=184,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64333333 0.64833333 0.77166667 0.72333333 0.80537975]
----------------------------------------
Trial 3579
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59 0.63333333 0.73166667 0.72666667 0.72863924]
----------------------------------------
Trial 3580
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=51,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77583333 0.7525 0.79333333 0.85083333 0.85759494]
----------------------------------------
Trial 3581
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67666667 0.69583333 0.79916667 0.78083333 0.89240506]
----------------------------------------
Trial 3582
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=167, random_state=100))])
cv score: [0.63208333 0.68916667 0.65666667 0.82416667 0.82871835]
----------------------------------------
Trial 3583
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69 0.73083333 0.77416667 0.86333333 0.86629747]
----------------------------------------
Trial 3584
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='sqrt',
n_estimators=53, random_state=100,
subsample=0.85))])
cv score: [0.56916667 0.62916667 0.6875 0.67 0.54113924]
----------------------------------------
Trial 3585
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=163, random_state=100,
subsample=0.7))])
cv score: [0.7875 0.67333333 0.7725 0.84833333 0.87658228]
----------------------------------------
Trial 3586
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=195,
random_state=100))])
cv score: [0.68166667 0.6375 0.69833333 0.795 0.82278481]
----------------------------------------
Trial 3587
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=131,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 3588
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=120,
random_state=100))])
cv score: [0.615 0.67666667 0.69166667 0.815 0.82990506]
----------------------------------------
Trial 3589
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='log2',
n_estimators=53, random_state=100,
subsample=0.75))])
cv score: [0.57125 0.67333333 0.6925 0.86 0.78955696]
----------------------------------------
Trial 3590
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, n_estimators=166,
random_state=100, subsample=0.9))])
cv score: [0.6975 0.64083333 0.76916667 0.7875 0.77610759]
----------------------------------------
Trial 3591
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='sqrt',
n_estimators=180, random_state=100,
subsample=0.8))])
cv score: [0.61333333 0.69166667 0.68583333 0.80833333 0.80300633]
----------------------------------------
Trial 3592
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=192,
random_state=100))])
cv score: [0.68708333 0.51375 0.82458333 0.66666667 0.66416139]
----------------------------------------
Trial 3593
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=86, random_state=100,
subsample=0.85))])
cv score: [0.61916667 0.565 0.715 0.77916667 0.68829114]
----------------------------------------
Trial 3594
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=54,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.72583333 0.7775 0.81 0.90585443]
----------------------------------------
Trial 3595
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=29,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.6925 0.72583333 0.75333333 0.81566456]
----------------------------------------
Trial 3596
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='log2',
n_estimators=82, random_state=100,
subsample=0.95))])
cv score: [0.66916667 0.54333333 0.75 0.79666667 0.80458861]
----------------------------------------
Trial 3597
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=106,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6775 0.665 0.765 0.7675 0.82199367]
----------------------------------------
Trial 3598
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=158,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77333333 0.745 0.77708333 0.86 0.85047468]
----------------------------------------
Trial 3599
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=39, random_state=100,
subsample=0.95))])
cv score: [0.76666667 0.63916667 0.7675 0.84 0.84335443]
----------------------------------------
Trial 3600
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=169,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7025 0.65916667 0.7875 0.79333333 0.87658228]
----------------------------------------
Trial 3601
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=103, random_state=100,
subsample=0.6))])
cv score: [0.6275 0.525 0.675 0.70416667 0.68275316]
----------------------------------------
Trial 3602
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.85))])
cv score: [0.685 0.55333333 0.69666667 0.79666667 0.82120253]
----------------------------------------
Trial 3603
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=80,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61083333 0.71 0.745 0.74166667 0.71598101]
----------------------------------------
Trial 3604
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='log2',
n_estimators=138, random_state=100,
subsample=0.85))])
cv score: [0.68166667 0.5725 0.6875 0.82166667 0.82199367]
----------------------------------------
Trial 3605
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=42,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.74708333 0.79166667 0.8625 0.85047468]
----------------------------------------
Trial 3606
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
n_estimators=12, random_state=100,
subsample=0.6))])
cv score: [0.7675 0.70666667 0.73666667 0.74416667 0.76424051]
----------------------------------------
Trial 3607
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.645 0.65666667 0.75166667 0.75 0.83860759]
----------------------------------------
Trial 3608
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=180, random_state=100))])
cv score: [0.69416667 0.62083333 0.73916667 0.82125 0.82594937]
----------------------------------------
Trial 3609
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=184, random_state=100))])
cv score: [0.70333333 0.63 0.7225 0.79666667 0.82832278]
----------------------------------------
Trial 3610
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=134,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64416667 0.6475 0.76833333 0.78 0.84256329]
----------------------------------------
Trial 3611
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=135,
random_state=100))])
cv score: [0.70416667 0.59 0.685 0.76916667 0.74920886]
----------------------------------------
Trial 3612
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='log2',
n_estimators=84, random_state=100,
subsample=0.85))])
cv score: [0.58083333 0.5275 0.64833333 0.66166667 0.6289557 ]
----------------------------------------
Trial 3613
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=174, random_state=100))])
cv score: [0.68 0.62083333 0.72583333 0.8025 0.83386076]
----------------------------------------
Trial 3614
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=34,
random_state=100))])
cv score: [0.58625 0.69291667 0.70875 0.83125 0.82832278]
----------------------------------------
Trial 3615
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=84, random_state=100,
subsample=0.95))])
cv score: [0.68333333 0.5975 0.72666667 0.78416667 0.7943038 ]
----------------------------------------
Trial 3616
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=155,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.67916667 0.78416667 0.79166667 0.87420886]
----------------------------------------
Trial 3617
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=97,
random_state=100))])
cv score: [0.62833333 0.5925 0.735 0.7825 0.75830696]
----------------------------------------
Trial 3618
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
n_estimators=23, random_state=100,
subsample=0.95))])
cv score: [0.7675 0.68375 0.78708333 0.78583333 0.87420886]
----------------------------------------
Trial 3619
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=173, random_state=100))])
cv score: [0.67416667 0.62083333 0.72666667 0.80416667 0.8346519 ]
----------------------------------------
Trial 3620
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=106,
random_state=100))])
cv score: [0.57833333 0.665 0.69583333 0.83083333 0.83781646]
----------------------------------------
Trial 3621
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=33, random_state=100))])
cv score: [0.63333333 0.685 0.72041667 0.81916667 0.85047468]
----------------------------------------
Trial 3622
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=80,
random_state=100))])
cv score: [0.66583333 0.50541667 0.79666667 0.66291667 0.78876582]
----------------------------------------
Trial 3623
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=50, random_state=100,
subsample=0.7))])
cv score: [0.6625 0.70166667 0.65083333 0.83833333 0.82120253]
----------------------------------------
Trial 3624
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.735 0.71333333 0.77916667 0.81333333 0.90110759]
----------------------------------------
Trial 3625
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53791667 0.68708333 0.67625 0.80041667 0.80537975]
----------------------------------------
Trial 3626
----------------------------------------
Parameters {'xgb__n_estimators': 95, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=95,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61583333 0.69666667 0.77833333 0.64833333 0.86313291]
----------------------------------------
Trial 3627
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=97,
random_state=100))])
cv score: [0.51208333 0.65708333 0.71083333 0.83 0.76661392]
----------------------------------------
Trial 3628
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=92,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74666667 0.75041667 0.78208333 0.85416667 0.85917722]
----------------------------------------
Trial 3629
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=129,
random_state=100))])
cv score: [0.6825 0.58333333 0.69583333 0.79083333 0.79588608]
----------------------------------------
Trial 3630
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74833333 0.745 0.78958333 0.8625 0.87737342]
----------------------------------------
Trial 3631
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=190,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67333333 0.66416667 0.78333333 0.77666667 0.8568038 ]
----------------------------------------
Trial 3632
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67833333 0.64416667 0.755 0.76833333 0.78560127]
----------------------------------------
Trial 3633
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='sqrt',
n_estimators=36, random_state=100,
subsample=0.95))])
cv score: [0.61416667 0.53833333 0.62166667 0.76666667 0.68512658]
----------------------------------------
Trial 3634
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78583333 0.73791667 0.77416667 0.84333333 0.875 ]
----------------------------------------
Trial 3635
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=170,
random_state=100))])
cv score: [0.64208333 0.57583333 0.73625 0.78291667 0.79193038]
----------------------------------------
Trial 3636
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='sqrt',
n_estimators=116, random_state=100,
subsample=0.95))])
cv score: [0.68083333 0.6225 0.7075 0.80166667 0.81408228]
----------------------------------------
Trial 3637
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=114,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59666667 0.6275 0.72916667 0.73666667 0.75158228]
----------------------------------------
Trial 3638
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=166,
random_state=100))])
cv score: [0.68666667 0.62666667 0.6975 0.80333333 0.83544304]
----------------------------------------
Trial 3639
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66583333 0.73083333 0.77416667 0.74916667 0.85363924]
----------------------------------------
Trial 3640
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=174,
random_state=100))])
cv score: [0.63791667 0.57583333 0.73291667 0.79 0.78955696]
----------------------------------------
Trial 3641
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=97,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6075 0.71125 0.73583333 0.85791667 0.80221519]
----------------------------------------
Trial 3642
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=190,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7625 0.745 0.77916667 0.86 0.85126582]
----------------------------------------
Trial 3643
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62833333 0.63583333 0.8 0.765 0.84098101]
----------------------------------------
Trial 3644
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, n_estimators=14,
random_state=100, subsample=0.9))])
cv score: [0.75833333 0.63458333 0.7025 0.83958333 0.75712025]
----------------------------------------
Trial 3645
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=79, random_state=100,
subsample=0.8))])
cv score: [0.63166667 0.58083333 0.6575 0.72333333 0.63053797]
----------------------------------------
Trial 3646
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='sqrt',
n_estimators=78, random_state=100,
subsample=0.7))])
cv score: [0.66166667 0.59583333 0.70333333 0.7825 0.71914557]
----------------------------------------
Trial 3647
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=20, random_state=100,
subsample=0.9))])
cv score: [0.69333333 0.66416667 0.74083333 0.7975 0.67879747]
----------------------------------------
Trial 3648
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=43, random_state=100,
subsample=0.9))])
cv score: [0.65083333 0.63833333 0.7325 0.725 0.75553797]
----------------------------------------
Trial 3649
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=27,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73833333 0.71916667 0.7875 0.82916667 0.87658228]
----------------------------------------
Trial 3650
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=41, random_state=100,
subsample=0.6))])
cv score: [0.7625 0.70333333 0.73 0.81416667 0.79193038]
----------------------------------------
Trial 3651
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=64, random_state=100,
subsample=0.95))])
cv score: [0.71583333 0.64416667 0.82458333 0.8175 0.79825949]
----------------------------------------
Trial 3652
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=79, random_state=100,
subsample=0.65))])
cv score: [0.69833333 0.605 0.71166667 0.82583333 0.83148734]
----------------------------------------
Trial 3653
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=140,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74166667 0.72916667 0.79333333 0.86333333 0.88053797]
----------------------------------------
Trial 3654
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=158,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76583333 0.73416667 0.77166667 0.84333333 0.92009494]
----------------------------------------
Trial 3655
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=175,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6975 0.66833333 0.80666667 0.80583333 0.87262658]
----------------------------------------
Trial 3656
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=81, random_state=100,
subsample=0.6))])
cv score: [0.63166667 0.4675 0.56916667 0.54416667 0.71993671]
----------------------------------------
Trial 3657
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69375 0.72666667 0.77541667 0.85583333 0.89517405]
----------------------------------------
Trial 3658
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
random_state=100))])
cv score: [0.64458333 0.695 0.64 0.82166667 0.81170886]
----------------------------------------
Trial 3659
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=71,
random_state=100))])
cv score: [0.66583333 0.50541667 0.79666667 0.66333333 0.78876582]
----------------------------------------
Trial 3660
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=77, random_state=100))])
cv score: [0.57166667 0.72458333 0.68833333 0.78916667 0.79825949]
----------------------------------------
Trial 3661
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=189, random_state=100,
subsample=0.75))])
cv score: [0.74416667 0.67416667 0.81583333 0.84166667 0.82357595]
----------------------------------------
Trial 3662
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='log2',
n_estimators=148, random_state=100,
subsample=0.6))])
cv score: [0.5875 0.665 0.62166667 0.69666667 0.75079114]
----------------------------------------
Trial 3663
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74166667 0.69083333 0.76083333 0.81333333 0.90268987]
----------------------------------------
Trial 3664
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67666667 0.6775 0.78083333 0.78166667 0.86946203]
----------------------------------------
Trial 3665
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
n_estimators=74, random_state=100,
subsample=0.6))])
cv score: [0.75583333 0.7075 0.785 0.8525 0.84810127]
----------------------------------------
Trial 3666
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=25,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73666667 0.71625 0.75625 0.84833333 0.82594937]
----------------------------------------
Trial 3667
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='log2',
n_estimators=25, random_state=100,
subsample=0.6))])
cv score: [0.715 0.64166667 0.67833333 0.81333333 0.79588608]
----------------------------------------
Trial 3668
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=139, random_state=100))])
cv score: [0.695 0.6 0.7375 0.82166667 0.82515823]
----------------------------------------
Trial 3669
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=106,
random_state=100))])
cv score: [0.71583333 0.555 0.80958333 0.77583333 0.77017405]
----------------------------------------
Trial 3670
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72666667 0.7325 0.75166667 0.87 0.84414557]
----------------------------------------
Trial 3671
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=152,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.71 0.79333333 0.82583333 0.91851266]
----------------------------------------
Trial 3672
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=144, random_state=100,
subsample=0.8))])
cv score: [0.63333333 0.58166667 0.67 0.7725 0.77689873]
----------------------------------------
Trial 3673
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=38, random_state=100))])
cv score: [0.68583333 0.67583333 0.6825 0.80916667 0.86787975]
----------------------------------------
Trial 3674
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67791667 0.74 0.76416667 0.83958333 0.88291139]
----------------------------------------
Trial 3675
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=110,
random_state=100))])
cv score: [0.68166667 0.61083333 0.72083333 0.80916667 0.82278481]
----------------------------------------
Trial 3676
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=119,
random_state=100,
subsample=0.85))])
cv score: [0.71666667 0.63333333 0.7975 0.8325 0.79588608]
----------------------------------------
Trial 3677
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=184,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.66333333 0.79166667 0.78166667 0.8346519 ]
----------------------------------------
Trial 3678
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73833333 0.72166667 0.7775 0.85583333 0.87816456]
----------------------------------------
Trial 3679
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='sqrt',
n_estimators=160, random_state=100,
subsample=0.85))])
cv score: [0.7125 0.56083333 0.67416667 0.76583333 0.7096519 ]
----------------------------------------
Trial 3680
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=97, random_state=100))])
cv score: [0.67833333 0.64666667 0.72666667 0.79166667 0.84177215]
----------------------------------------
Trial 3681
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=129,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6475 0.65 0.76583333 0.735 0.7903481 ]
----------------------------------------
Trial 3682
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=12,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76583333 0.75541667 0.7675 0.74666667 0.77848101]
----------------------------------------
Trial 3683
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=67,
random_state=100))])
cv score: [0.67 0.58875 0.70333333 0.795 0.83386076]
----------------------------------------
Trial 3684
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=61,
random_state=100))])
cv score: [0.65666667 0.59083333 0.695 0.8 0.84889241]
----------------------------------------
Trial 3685
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66666667 0.67333333 0.80583333 0.80833333 0.81566456]
----------------------------------------
Trial 3686
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=117,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6075 0.69833333 0.7675 0.72333333 0.8528481 ]
----------------------------------------
Trial 3687
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=24,
random_state=100))])
cv score: [0.655 0.64083333 0.82916667 0.78083333 0.84098101]
----------------------------------------
Trial 3688
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=99, random_state=100,
subsample=0.95))])
cv score: [0.6475 0.61416667 0.70583333 0.7575 0.76503165]
----------------------------------------
Trial 3689
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=35, random_state=100,
subsample=0.65))])
cv score: [0.6425 0.65083333 0.69166667 0.76916667 0.79113924]
----------------------------------------
Trial 3690
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='log2',
n_estimators=114, random_state=100,
subsample=0.85))])
cv score: [0.61166667 0.55166667 0.685 0.74666667 0.71914557]
----------------------------------------
Trial 3691
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=24,
random_state=100))])
cv score: [0.72208333 0.57333333 0.68458333 0.7725 0.80458861]
----------------------------------------
Trial 3692
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=107, random_state=100,
subsample=0.6))])
cv score: [0.48458333 0.57 0.51541667 0.56666667 0.48813291]
----------------------------------------
Trial 3693
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='log2',
n_estimators=167, random_state=100,
subsample=0.8))])
cv score: [0.74166667 0.61083333 0.74416667 0.82583333 0.80696203]
----------------------------------------
Trial 3694
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=15, random_state=100,
subsample=0.75))])
cv score: [0.49333333 0.58 0.7 0.74916667 0.73022152]
----------------------------------------
Trial 3695
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='sqrt',
n_estimators=167, random_state=100,
subsample=0.75))])
cv score: [0.70083333 0.62833333 0.7075 0.81833333 0.77136076]
----------------------------------------
Trial 3696
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=106,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76166667 0.7175 0.7825 0.84 0.86946203]
----------------------------------------
Trial 3697
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=123,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64416667 0.64083333 0.73833333 0.74083333 0.81962025]
----------------------------------------
Trial 3698
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=119,
random_state=100))])
cv score: [0.695 0.63916667 0.6975 0.80333333 0.82515823]
----------------------------------------
Trial 3699
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=61,
random_state=100))])
cv score: [0.65583333 0.5825 0.68666667 0.79125 0.81566456]
----------------------------------------
Trial 3700
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=147, random_state=100,
subsample=0.85))])
cv score: [0.62666667 0.57583333 0.705 0.80583333 0.74287975]
----------------------------------------
Trial 3701
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=75, random_state=100))])
cv score: [0.6325 0.6775 0.75583333 0.7875 0.84018987]
----------------------------------------
Trial 3702
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=195,
random_state=100))])
cv score: [0.66833333 0.58333333 0.7175 0.80666667 0.8085443 ]
----------------------------------------
Trial 3703
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=93, random_state=100,
subsample=0.75))])
cv score: [0.49666667 0.57916667 0.59083333 0.7525 0.73971519]
----------------------------------------
Trial 3704
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=13,
random_state=100))])
cv score: [0.58083333 0.58416667 0.66041667 0.82791667 0.84968354]
----------------------------------------
Trial 3705
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=157, random_state=100))])
cv score: [0.77833333 0.69666667 0.77416667 0.8525 0.87341772]
----------------------------------------
Trial 3706
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=138,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.71666667 0.78666667 0.82666667 0.91455696]
----------------------------------------
Trial 3707
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=135,
random_state=100))])
cv score: [0.69333333 0.62083333 0.69 0.795 0.81724684]
----------------------------------------
Trial 3708
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='log2',
n_estimators=79, random_state=100,
subsample=0.95))])
cv score: [0.69083333 0.54916667 0.6975 0.7775 0.77610759]
----------------------------------------
Trial 3709
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=33,
random_state=100))])
cv score: [0.69041667 0.48916667 0.77958333 0.67125 0.63924051]
----------------------------------------
Trial 3710
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=163,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.74458333 0.78625 0.86 0.85601266]
----------------------------------------
Trial 3711
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(n_estimators=18,
random_state=100))])
cv score: [0.72458333 0.71291667 0.7625 0.83375 0.89517405]
----------------------------------------
Trial 3712
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=166,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70833333 0.6875 0.77916667 0.80833333 0.87816456]
----------------------------------------
Trial 3713
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=178,
random_state=100,
subsample=0.95))])
cv score: [0.6925 0.655 0.77916667 0.825 0.76186709]
----------------------------------------
Trial 3714
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='sqrt',
n_estimators=170, random_state=100,
subsample=0.95))])
cv score: [0.64 0.57166667 0.66916667 0.7725 0.73259494]
----------------------------------------
Trial 3715
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77583333 0.72583333 0.7825 0.85083333 0.88924051]
----------------------------------------
Trial 3716
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=172, random_state=100,
subsample=0.6))])
cv score: [0.51916667 0.6225 0.61 0.68416667 0.72072785]
----------------------------------------
Trial 3717
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=35, random_state=100,
subsample=0.65))])
cv score: [0.565 0.64916667 0.77666667 0.6825 0.8125 ]
----------------------------------------
Trial 3718
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=66, random_state=100,
subsample=0.95))])
cv score: [0.47 0.53916667 0.6325 0.69916667 0.71914557]
----------------------------------------
Trial 3719
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='log2',
n_estimators=135, random_state=100,
subsample=0.9))])
cv score: [0.66166667 0.61666667 0.6975 0.75416667 0.6835443 ]
----------------------------------------
Trial 3720
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=110,
random_state=100))])
cv score: [0.6975 0.63833333 0.70416667 0.80583333 0.82674051]
----------------------------------------
Trial 3721
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=82, random_state=100))])
cv score: [0.74333333 0.68 0.77416667 0.82833333 0.85126582]
----------------------------------------
Trial 3722
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77 0.72083333 0.78666667 0.8425 0.88132911]
----------------------------------------
Trial 3723
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=110,
random_state=100))])
cv score: [0.68166667 0.61083333 0.72083333 0.80916667 0.82278481]
----------------------------------------
Trial 3724
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=14, random_state=100))])
cv score: [0.72833333 0.64291667 0.76 0.87833333 0.83544304]
----------------------------------------
Trial 3725
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=145, random_state=100,
subsample=0.8))])
cv score: [0.51333333 0.5925 0.74166667 0.65666667 0.69699367]
----------------------------------------
Trial 3726
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=55,
random_state=100))])
cv score: [0.62125 0.58291667 0.68791667 0.76583333 0.83781646]
----------------------------------------
Trial 3727
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=146,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66083333 0.65416667 0.77416667 0.74083333 0.81962025]
----------------------------------------
Trial 3728
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=178,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.685 0.68583333 0.76166667 0.78416667 0.84335443]
----------------------------------------
Trial 3729
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.64583333 0.7475 0.80083333 0.81091772]
----------------------------------------
Trial 3730
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=47, random_state=100))])
cv score: [0.66666667 0.63333333 0.69666667 0.76916667 0.84651899]
----------------------------------------
Trial 3731
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=32,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.67642405]
----------------------------------------
Trial 3732
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=167, random_state=100,
subsample=0.7))])
cv score: [0.66833333 0.6175 0.72083333 0.76083333 0.85601266]
----------------------------------------
Trial 3733
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=32,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70708333 0.74916667 0.73333333 0.86666667 0.82357595]
----------------------------------------
Trial 3734
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=36, random_state=100,
subsample=0.9))])
cv score: [0.63833333 0.62416667 0.75833333 0.78416667 0.70490506]
----------------------------------------
Trial 3735
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=87,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58416667 0.66916667 0.76166667 0.75 0.84889241]
----------------------------------------
Trial 3736
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=192,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74666667 0.7525 0.76958333 0.87 0.84651899]
----------------------------------------
Trial 3737
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=34,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69833333 0.715 0.78916667 0.8675 0.89161392]
----------------------------------------
Trial 3738
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=156, random_state=100))])
cv score: [0.57416667 0.70833333 0.69666667 0.81083333 0.79984177]
----------------------------------------
Trial 3739
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=22,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73083333 0.6975 0.7925 0.87583333 0.85363924]
----------------------------------------
Trial 3740
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63666667 0.62833333 0.74666667 0.73666667 0.7681962 ]
----------------------------------------
Trial 3741
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.65166667 0.715 0.71166667 0.80221519]
----------------------------------------
Trial 3742
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=70,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.72291667 0.77291667 0.85666667 0.8710443 ]
----------------------------------------
Trial 3743
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=137, random_state=100))])
cv score: [0.6825 0.62166667 0.755 0.82333333 0.8255538 ]
----------------------------------------
Trial 3744
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.69916667 0.77916667 0.80416667 0.87341772]
----------------------------------------
Trial 3745
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='sqrt',
n_estimators=150, random_state=100,
subsample=0.8))])
cv score: [0.68 0.64416667 0.715 0.82083333 0.82911392]
----------------------------------------
Trial 3746
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=112,
random_state=100))])
cv score: [0.63666667 0.6025 0.73083333 0.78041667 0.75791139]
----------------------------------------
Trial 3747
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=91, random_state=100))])
cv score: [0.70666667 0.62958333 0.7325 0.81625 0.80300633]
----------------------------------------
Trial 3748
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=190, random_state=100,
subsample=0.65))])
cv score: [0.61916667 0.66833333 0.69833333 0.83666667 0.80142405]
----------------------------------------
Trial 3749
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=46,
random_state=100))])
cv score: [0.67583333 0.68166667 0.7375 0.82375 0.84177215]
----------------------------------------
Trial 3750
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=172, random_state=100,
subsample=0.8))])
cv score: [0.50083333 0.5875 0.75166667 0.65416667 0.70015823]
----------------------------------------
Trial 3751
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=34,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 3752
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.705 0.72875 0.7525 0.85125 0.83504747]
----------------------------------------
Trial 3753
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=104, random_state=100,
subsample=0.75))])
cv score: [0.69 0.63333333 0.71416667 0.84333333 0.85996835]
----------------------------------------
Trial 3754
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=95, random_state=100,
subsample=0.8))])
cv score: [0.68333333 0.6125 0.6825 0.7725 0.77373418]
----------------------------------------
Trial 3755
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73541667 0.73708333 0.80541667 0.84541667 0.90348101]
----------------------------------------
Trial 3756
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=121, random_state=100,
subsample=0.85))])
cv score: [0.78416667 0.68208333 0.72791667 0.85666667 0.85601266]
----------------------------------------
Trial 3757
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=93, random_state=100,
subsample=0.85))])
cv score: [0.5175 0.60916667 0.62083333 0.70833333 0.75316456]
----------------------------------------
Trial 3758
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=183,
random_state=100))])
cv score: [0.66166667 0.63833333 0.72166667 0.76833333 0.77689873]
----------------------------------------
Trial 3759
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6325 0.68416667 0.79583333 0.75583333 0.85838608]
----------------------------------------
Trial 3760
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=121,
random_state=100))])
cv score: [0.6125 0.67416667 0.69583333 0.8175 0.8306962 ]
----------------------------------------
Trial 3761
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=199,
random_state=100))])
cv score: [0.6275 0.6625 0.705 0.82 0.82911392]
----------------------------------------
Trial 3762
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=116, random_state=100))])
cv score: [0.70083333 0.605 0.73166667 0.82291667 0.82674051]
----------------------------------------
Trial 3763
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=41,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77 0.71333333 0.80583333 0.83916667 0.84810127]
----------------------------------------
Trial 3764
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=174, random_state=100))])
cv score: [0.6925 0.645 0.7125 0.81166667 0.84177215]
----------------------------------------
Trial 3765
----------------------------------------
Parameters {'xgb__n_estimators': 160, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=160,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61666667 0.505 0.745 0.73333333 0.71123418]
----------------------------------------
Trial 3766
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61333333 0.67083333 0.73916667 0.69083333 0.80537975]
----------------------------------------
Trial 3767
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=192, random_state=100))])
cv score: [0.68666667 0.63208333 0.7225 0.7975 0.83939873]
----------------------------------------
Trial 3768
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=84,
random_state=100))])
cv score: [0.51625 0.67541667 0.71666667 0.81583333 0.76344937]
----------------------------------------
Trial 3769
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, n_estimators=67,
random_state=100,
subsample=0.65))])
cv score: [0.6975 0.67583333 0.75833333 0.8425 0.7721519 ]
----------------------------------------
Trial 3770
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=22,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78916667 0.69333333 0.79916667 0.8375 0.88686709]
----------------------------------------
Trial 3771
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='log2',
n_estimators=113, random_state=100,
subsample=0.75))])
cv score: [0.61166667 0.61416667 0.66333333 0.73916667 0.76740506]
----------------------------------------
Trial 3772
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=12, random_state=100,
subsample=0.65))])
cv score: [0.76583333 0.69916667 0.78583333 0.81583333 0.81210443]
----------------------------------------
Trial 3773
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68083333 0.72541667 0.76208333 0.86083333 0.86313291]
----------------------------------------
Trial 3774
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='sqrt',
n_estimators=60, random_state=100,
subsample=0.65))])
cv score: [0.62666667 0.65833333 0.69416667 0.80416667 0.77136076]
----------------------------------------
Trial 3775
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=22,
random_state=100))])
cv score: [0.58083333 0.65916667 0.65708333 0.82958333 0.82871835]
----------------------------------------
Trial 3776
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=17, random_state=100))])
cv score: [0.76666667 0.66666667 0.68791667 0.85458333 0.8710443 ]
----------------------------------------
Trial 3777
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=60, random_state=100,
subsample=0.75))])
cv score: [0.6525 0.61333333 0.685 0.81083333 0.79351266]
----------------------------------------
Trial 3778
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
n_estimators=166, random_state=100,
subsample=0.75))])
cv score: [0.68416667 0.65416667 0.7525 0.805 0.83306962]
----------------------------------------
Trial 3779
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=128,
random_state=100))])
cv score: [0.50875 0.67458333 0.70291667 0.81 0.76265823]
----------------------------------------
Trial 3780
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=45, random_state=100,
subsample=0.9))])
cv score: [0.66833333 0.67166667 0.60458333 0.75458333 0.80458861]
----------------------------------------
Trial 3781
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=31,
random_state=100))])
cv score: [0.705 0.6625 0.71083333 0.78583333 0.7056962 ]
----------------------------------------
Trial 3782
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=43, random_state=100,
subsample=0.7))])
cv score: [0.70166667 0.68083333 0.71583333 0.83 0.85996835]
----------------------------------------
Trial 3783
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=178, random_state=100))])
cv score: [0.71041667 0.6125 0.73416667 0.82708333 0.80537975]
----------------------------------------
Trial 3784
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=45, random_state=100,
subsample=0.6))])
cv score: [0.75 0.72333333 0.76583333 0.84416667 0.84968354]
----------------------------------------
Trial 3785
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=87,
random_state=100))])
cv score: [0.6925 0.4875 0.72041667 0.66875 0.63607595]
----------------------------------------
Trial 3786
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=197,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71666667 0.72166667 0.765 0.86833333 0.8568038 ]
----------------------------------------
Trial 3787
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69833333 0.66916667 0.795 0.81083333 0.87183544]
----------------------------------------
Trial 3788
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=145,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78083333 0.72833333 0.785 0.8625 0.88370253]
----------------------------------------
Trial 3789
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=116, random_state=100))])
cv score: [0.64541667 0.69416667 0.655 0.8175 0.82199367]
----------------------------------------
Trial 3790
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=153,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 3791
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=62, random_state=100,
subsample=0.7))])
cv score: [0.62916667 0.665 0.70416667 0.83833333 0.81882911]
----------------------------------------
Trial 3792
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=17, random_state=100))])
cv score: [0.60333333 0.64083333 0.67583333 0.70041667 0.82753165]
----------------------------------------
Trial 3793
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=68, random_state=100,
subsample=0.9))])
cv score: [0.63416667 0.58833333 0.66833333 0.735 0.77768987]
----------------------------------------
Trial 3794
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.67333333 0.79833333 0.7875 0.85917722]
----------------------------------------
Trial 3795
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69 0.645 0.69916667 0.74916667 0.76186709]
----------------------------------------
Trial 3796
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=136, random_state=100))])
cv score: [0.68833333 0.6575 0.73083333 0.815 0.85996835]
----------------------------------------
Trial 3797
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=128, random_state=100,
subsample=0.95))])
cv score: [0.80166667 0.66375 0.76083333 0.84166667 0.85996835]
----------------------------------------
Trial 3798
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57708333 0.65083333 0.66166667 0.79333333 0.75079114]
----------------------------------------
Trial 3799
----------------------------------------
Parameters {'xgb__n_estimators': 99, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=99,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7375 0.68333333 0.80416667 0.8225 0.88607595]
----------------------------------------
Trial 3800
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=41, random_state=100,
subsample=0.7))])
cv score: [0.46333333 0.60666667 0.59833333 0.5175 0.71677215]
----------------------------------------
Trial 3801
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='sqrt',
n_estimators=149, random_state=100,
subsample=0.95))])
cv score: [0.68 0.6 0.71583333 0.76583333 0.78006329]
----------------------------------------
Trial 3802
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6925 0.7225 0.77458333 0.85666667 0.86234177]
----------------------------------------
Trial 3803
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65583333 0.71 0.71208333 0.85416667 0.82199367]
----------------------------------------
Trial 3804
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
n_estimators=188, random_state=100,
subsample=0.65))])
cv score: [0.72916667 0.67 0.78083333 0.84 0.82911392]
----------------------------------------
Trial 3805
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=180, random_state=100,
subsample=0.6))])
cv score: [0.74833333 0.68333333 0.72916667 0.875 0.87974684]
----------------------------------------
Trial 3806
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=98, random_state=100,
subsample=0.6))])
cv score: [0.60083333 0.63 0.64833333 0.6425 0.62420886]
----------------------------------------
Trial 3807
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=147, random_state=100))])
cv score: [0.745 0.67958333 0.78333333 0.8325 0.84414557]
----------------------------------------
Trial 3808
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=66, random_state=100,
subsample=0.6))])
cv score: [0.74583333 0.65333333 0.73666667 0.81333333 0.80933544]
----------------------------------------
Trial 3809
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='sqrt',
n_estimators=105, random_state=100,
subsample=0.75))])
cv score: [0.64833333 0.59666667 0.66 0.80083333 0.76503165]
----------------------------------------
Trial 3810
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.7))])
cv score: [0.70416667 0.64916667 0.71333333 0.79583333 0.82674051]
----------------------------------------
Trial 3811
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=198, random_state=100,
subsample=0.85))])
cv score: [0.65833333 0.65 0.75666667 0.705 0.72468354]
----------------------------------------
Trial 3812
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67583333 0.72916667 0.73833333 0.85958333 0.8477057 ]
----------------------------------------
Trial 3813
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=40, random_state=100,
subsample=0.95))])
cv score: [0.65416667 0.5675 0.65083333 0.77833333 0.81408228]
----------------------------------------
Trial 3814
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76875 0.73833333 0.78083333 0.86208333 0.85126582]
----------------------------------------
Trial 3815
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=90, random_state=100))])
cv score: [0.69666667 0.66416667 0.73083333 0.79666667 0.86550633]
----------------------------------------
Trial 3816
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=51,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75333333 0.69416667 0.78583333 0.79666667 0.85522152]
----------------------------------------
Trial 3817
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=38,
random_state=100))])
cv score: [0.6625 0.5925 0.6875 0.79833333 0.82674051]
----------------------------------------
Trial 3818
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72916667 0.73416667 0.75958333 0.84625 0.90348101]
----------------------------------------
Trial 3819
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=155, random_state=100,
subsample=0.8))])
cv score: [0.74083333 0.63833333 0.8025 0.84833333 0.80458861]
----------------------------------------
Trial 3820
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=24,
random_state=100))])
cv score: [0.6875 0.55625 0.70833333 0.83416667 0.84889241]
----------------------------------------
Trial 3821
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=181, random_state=100))])
cv score: [0.70958333 0.61041667 0.73125 0.82291667 0.80379747]
----------------------------------------
Trial 3822
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=71, random_state=100,
subsample=0.65))])
cv score: [0.655 0.58583333 0.6675 0.75416667 0.76661392]
----------------------------------------
Trial 3823
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=62, random_state=100,
subsample=0.6))])
cv score: [0.59833333 0.57166667 0.615 0.79083333 0.79746835]
----------------------------------------
Trial 3824
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=175, random_state=100,
subsample=0.95))])
cv score: [0.54416667 0.55416667 0.6525 0.6825 0.6693038 ]
----------------------------------------
Trial 3825
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=189,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64 0.635 0.76333333 0.76 0.8568038 ]
----------------------------------------
Trial 3826
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, n_estimators=133,
random_state=100,
subsample=0.95))])
cv score: [0.69833333 0.65166667 0.76333333 0.82 0.76977848]
----------------------------------------
Trial 3827
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=126,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.655 0.63083333 0.75083333 0.74583333 0.82990506]
----------------------------------------
Trial 3828
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=35, random_state=100,
subsample=0.85))])
cv score: [0.67916667 0.62875 0.82333333 0.79 0.82594937]
----------------------------------------
Trial 3829
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=135,
random_state=100))])
cv score: [0.6525 0.66666667 0.7025 0.81666667 0.84335443]
----------------------------------------
Trial 3830
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59083333 0.66083333 0.72333333 0.71833333 0.77452532]
----------------------------------------
Trial 3831
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=154, random_state=100))])
cv score: [0.69666667 0.61166667 0.72833333 0.81875 0.83227848]
----------------------------------------
Trial 3832
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67833333 0.675 0.76666667 0.78 0.87183544]
----------------------------------------
Trial 3833
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='log2',
n_estimators=47,
random_state=100))])
cv score: [0.63333333 0.62583333 0.71916667 0.7925 0.80933544]
----------------------------------------
Trial 3834
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='log2',
n_estimators=198, random_state=100,
subsample=0.65))])
cv score: [0.67 0.63583333 0.6825 0.80583333 0.82436709]
----------------------------------------
Trial 3835
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='sqrt',
n_estimators=74, random_state=100,
subsample=0.8))])
cv score: [0.7025 0.5575 0.71166667 0.7875 0.73417722]
----------------------------------------
Trial 3836
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=188, random_state=100))])
cv score: [0.70833333 0.62916667 0.72916667 0.79666667 0.82594937]
----------------------------------------
Trial 3837
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=93, random_state=100,
subsample=0.95))])
cv score: [0.67833333 0.6375 0.70916667 0.78083333 0.81487342]
----------------------------------------
Trial 3838
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=180, random_state=100))])
cv score: [0.76833333 0.68791667 0.7775 0.84083333 0.84968354]
----------------------------------------
Trial 3839
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72833333 0.68833333 0.79833333 0.82583333 0.87974684]
----------------------------------------
Trial 3840
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=73,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79 0.73083333 0.76833333 0.85333333 0.9153481 ]
----------------------------------------
Trial 3841
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=102,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62333333 0.66416667 0.76666667 0.74916667 0.8164557 ]
----------------------------------------
Trial 3842
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
n_estimators=141, random_state=100,
subsample=0.95))])
cv score: [0.68333333 0.59166667 0.67583333 0.75 0.79113924]
----------------------------------------
Trial 3843
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=96,
random_state=100))])
cv score: [0.51291667 0.65708333 0.71083333 0.83 0.7681962 ]
----------------------------------------
Trial 3844
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=157, random_state=100))])
cv score: [0.77 0.68958333 0.77916667 0.8375 0.85205696]
----------------------------------------
Trial 3845
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=10, random_state=100))])
cv score: [0.68083333 0.6525 0.62833333 0.74416667 0.82594937]
----------------------------------------
Trial 3846
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=146,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59125 0.68 0.64916667 0.78208333 0.77096519]
----------------------------------------
Trial 3847
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='log2',
n_estimators=38, random_state=100,
subsample=0.65))])
cv score: [0.67583333 0.67916667 0.735 0.82083333 0.80221519]
----------------------------------------
Trial 3848
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=196, random_state=100))])
cv score: [0.665 0.6625 0.70416667 0.82583333 0.86313291]
----------------------------------------
Trial 3849
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=118, random_state=100))])
cv score: [0.65666667 0.6575 0.70333333 0.82416667 0.85601266]
----------------------------------------
Trial 3850
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=35, random_state=100))])
cv score: [0.69666667 0.575 0.74833333 0.81083333 0.82357595]
----------------------------------------
Trial 3851
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='log2',
n_estimators=142, random_state=100,
subsample=0.75))])
cv score: [0.62 0.69666667 0.69833333 0.8225 0.79984177]
----------------------------------------
Trial 3852
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=120, random_state=100))])
cv score: [0.67916667 0.63083333 0.74291667 0.825 0.82436709]
----------------------------------------
Trial 3853
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='log2',
n_estimators=130, random_state=100,
subsample=0.8))])
cv score: [0.64666667 0.59333333 0.7425 0.81083333 0.79193038]
----------------------------------------
Trial 3854
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77833333 0.725 0.775 0.8575 0.88528481]
----------------------------------------
Trial 3855
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=192,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79916667 0.66375 0.78876582]
----------------------------------------
Trial 3856
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=16, random_state=100))])
cv score: [0.71041667 0.6425 0.77208333 0.85875 0.81526899]
----------------------------------------
Trial 3857
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=29,
random_state=100))])
cv score: [0.74458333 0.61541667 0.7875 0.7025 0.76028481]
----------------------------------------
Trial 3858
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=155,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73958333 0.72833333 0.78458333 0.81833333 0.84454114]
----------------------------------------
Trial 3859
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=31, random_state=100,
subsample=0.85))])
cv score: [0.64416667 0.62666667 0.69083333 0.8125 0.81170886]
----------------------------------------
Trial 3860
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=76,
random_state=100))])
cv score: [0.685 0.59375 0.83041667 0.71125 0.78125 ]
----------------------------------------
Trial 3861
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=20,
random_state=100))])
cv score: [0.53583333 0.60083333 0.67166667 0.84291667 0.79153481]
----------------------------------------
Trial 3862
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.70083333 0.77083333 0.80416667 0.8931962 ]
----------------------------------------
Trial 3863
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=104,
random_state=100))])
cv score: [0.68708333 0.51458333 0.82458333 0.66333333 0.66574367]
----------------------------------------
Trial 3864
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='log2',
n_estimators=35, random_state=100,
subsample=0.6))])
cv score: [0.68416667 0.605 0.71333333 0.85666667 0.89082278]
----------------------------------------
Trial 3865
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
n_estimators=91, random_state=100,
subsample=0.95))])
cv score: [0.79166667 0.66791667 0.76791667 0.87041667 0.83267405]
----------------------------------------
Trial 3866
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=189,
random_state=100))])
cv score: [0.56375 0.66458333 0.70666667 0.82041667 0.81408228]
----------------------------------------
Trial 3867
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='log2',
n_estimators=192, random_state=100,
subsample=0.65))])
cv score: [0.69416667 0.60083333 0.70166667 0.80166667 0.80221519]
----------------------------------------
Trial 3868
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=71,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 3869
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=16, random_state=100,
subsample=0.6))])
cv score: [0.7325 0.73958333 0.79333333 0.71916667 0.81091772]
----------------------------------------
Trial 3870
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=68,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.74583333 0.79166667 0.86 0.84810127]
----------------------------------------
Trial 3871
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=142,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66 0.70916667 0.71416667 0.85083333 0.82199367]
----------------------------------------
Trial 3872
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=142, random_state=100,
subsample=0.95))])
cv score: [0.5 0.545 0.67333333 0.72583333 0.74841772]
----------------------------------------
Trial 3873
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=102, random_state=100))])
cv score: [0.70833333 0.6175 0.72666667 0.81291667 0.79390823]
----------------------------------------
Trial 3874
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=176,
random_state=100))])
cv score: [0.655 0.66833333 0.69833333 0.82833333 0.84177215]
----------------------------------------
Trial 3875
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=174, random_state=100))])
cv score: [0.77166667 0.68958333 0.77833333 0.84 0.85047468]
----------------------------------------
Trial 3876
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=89,
random_state=100))])
cv score: [0.58333333 0.66333333 0.695 0.82833333 0.83544304]
----------------------------------------
Trial 3877
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=79, random_state=100))])
cv score: [0.77583333 0.68416667 0.75583333 0.84 0.875 ]
----------------------------------------
Trial 3878
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=170,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67583333 0.67666667 0.7825 0.78916667 0.86708861]
----------------------------------------
Trial 3879
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.67166667 0.79666667 0.79 0.86787975]
----------------------------------------
Trial 3880
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=61, random_state=100,
subsample=0.6))])
cv score: [0.75833333 0.61416667 0.72583333 0.8075 0.77136076]
----------------------------------------
Trial 3881
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=144,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 3882
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=179, random_state=100,
subsample=0.65))])
cv score: [0.66416667 0.66666667 0.76 0.80833333 0.82041139]
----------------------------------------
Trial 3883
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=34, random_state=100))])
cv score: [0.6825 0.6225 0.715 0.77166667 0.82990506]
----------------------------------------
Trial 3884
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=176,
random_state=100))])
cv score: [0.6825 0.5875 0.69833333 0.80166667 0.81170886]
----------------------------------------
Trial 3885
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=191, random_state=100))])
cv score: [0.7225 0.675 0.78041667 0.83791667 0.81922468]
----------------------------------------
Trial 3886
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=61, random_state=100,
subsample=0.95))])
cv score: [0.65083333 0.5975 0.725 0.77416667 0.79588608]
----------------------------------------
Trial 3887
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=75,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72375 0.72 0.75458333 0.85166667 0.85759494]
----------------------------------------
Trial 3888
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=186, random_state=100,
subsample=0.7))])
cv score: [0.6375 0.63416667 0.64833333 0.78833333 0.63686709]
----------------------------------------
Trial 3889
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=89,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65583333 0.6725 0.77166667 0.72166667 0.81329114]
----------------------------------------
Trial 3890
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=177, random_state=100))])
cv score: [0.72333333 0.675 0.77708333 0.8375 0.82832278]
----------------------------------------
Trial 3891
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=172, random_state=100))])
cv score: [0.5725 0.70166667 0.70333333 0.80833333 0.8164557 ]
----------------------------------------
Trial 3892
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.9))])
cv score: [0.505 0.58416667 0.65 0.70083333 0.7318038 ]
----------------------------------------
Trial 3893
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=167, random_state=100))])
cv score: [0.77583333 0.68 0.74583333 0.85083333 0.89952532]
----------------------------------------
Trial 3894
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=186,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61833333 0.605 0.70583333 0.70916667 0.7056962 ]
----------------------------------------
Trial 3895
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=183,
random_state=100))])
cv score: [0.68708333 0.49375 0.80916667 0.64666667 0.75316456]
----------------------------------------
Trial 3896
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=164, random_state=100))])
cv score: [0.7475 0.67666667 0.7825 0.83416667 0.84968354]
----------------------------------------
Trial 3897
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70833333 0.75 0.76791667 0.86083333 0.84968354]
----------------------------------------
Trial 3898
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=57, random_state=100))])
cv score: [0.6675 0.61583333 0.7075 0.76916667 0.84572785]
----------------------------------------
Trial 3899
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.80666667 0.72 0.80666667 0.82666667 0.82832278]
----------------------------------------
Trial 3900
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='log2',
n_estimators=169, random_state=100,
subsample=0.6))])
cv score: [0.63 0.6725 0.69166667 0.78416667 0.84018987]
----------------------------------------
Trial 3901
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65916667 0.66166667 0.73083333 0.77666667 0.82199367]
----------------------------------------
Trial 3902
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='log2',
n_estimators=196, random_state=100,
subsample=0.8))])
cv score: [0.555 0.6375 0.67 0.69416667 0.74683544]
----------------------------------------
Trial 3903
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=192,
random_state=100))])
cv score: [0.56208333 0.66458333 0.70583333 0.82041667 0.81170886]
----------------------------------------
Trial 3904
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=153, random_state=100,
subsample=0.65))])
cv score: [0.53 0.64833333 0.68083333 0.785 0.77531646]
----------------------------------------
Trial 3905
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='sqrt',
n_estimators=101, random_state=100,
subsample=0.9))])
cv score: [0.665 0.57833333 0.7175 0.785 0.79905063]
----------------------------------------
Trial 3906
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74416667 0.70833333 0.76791667 0.85166667 0.87025316]
----------------------------------------
Trial 3907
----------------------------------------
Parameters {'xgb__n_estimators': 99, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=99,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76583333 0.73333333 0.77916667 0.85583333 0.88132911]
----------------------------------------
Trial 3908
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64833333 0.605 0.71333333 0.72791667 0.77294304]
----------------------------------------
Trial 3909
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=157,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58166667 0.66166667 0.73375 0.70291667 0.77294304]
----------------------------------------
Trial 3910
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.7))])
cv score: [0.68 0.60166667 0.71833333 0.785 0.79272152]
----------------------------------------
Trial 3911
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=38, random_state=100))])
cv score: [0.685 0.69166667 0.72416667 0.76416667 0.84018987]
----------------------------------------
Trial 3912
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=58, random_state=100,
subsample=0.65))])
cv score: [0.65583333 0.68 0.73416667 0.82916667 0.82911392]
----------------------------------------
Trial 3913
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=82,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63583333 0.64916667 0.76 0.72083333 0.73813291]
----------------------------------------
Trial 3914
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.46833333 0.56166667 0.50958333 0.7725 0.60324367]
----------------------------------------
Trial 3915
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
n_estimators=187, random_state=100,
subsample=0.9))])
cv score: [0.63416667 0.6125 0.76583333 0.77416667 0.73496835]
----------------------------------------
Trial 3916
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=175,
random_state=100))])
cv score: [0.63625 0.57583333 0.73375 0.79125 0.78955696]
----------------------------------------
Trial 3917
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=119,
random_state=100))])
cv score: [0.68583333 0.63 0.69 0.79166667 0.80617089]
----------------------------------------
Trial 3918
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='log2',
n_estimators=165, random_state=100,
subsample=0.75))])
cv score: [0.6575 0.625 0.67166667 0.73083333 0.76265823]
----------------------------------------
Trial 3919
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74416667 0.725 0.8025 0.83333333 0.88291139]
----------------------------------------
Trial 3920
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59416667 0.64666667 0.72583333 0.71333333 0.75791139]
----------------------------------------
Trial 3921
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='log2',
n_estimators=129,
random_state=100))])
cv score: [0.65916667 0.6 0.70583333 0.69166667 0.74920886]
----------------------------------------
Trial 3922
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='sqrt',
n_estimators=82,
random_state=100))])
cv score: [0.64166667 0.66041667 0.72333333 0.83708333 0.81962025]
----------------------------------------
Trial 3923
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=70, random_state=100))])
cv score: [0.64958333 0.60791667 0.70875 0.80375 0.81170886]
----------------------------------------
Trial 3924
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=70, random_state=100,
subsample=0.8))])
cv score: [0.65833333 0.6125 0.75083333 0.78416667 0.77136076]
----------------------------------------
Trial 3925
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=140, random_state=100,
subsample=0.8))])
cv score: [0.76583333 0.6475 0.79416667 0.83833333 0.83544304]
----------------------------------------
Trial 3926
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=195, random_state=100,
subsample=0.7))])
cv score: [0.6475 0.65666667 0.595 0.67083333 0.67009494]
----------------------------------------
Trial 3927
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='sqrt',
n_estimators=51, random_state=100,
subsample=0.85))])
cv score: [0.6475 0.66583333 0.67333333 0.70833333 0.72547468]
----------------------------------------
Trial 3928
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=130, random_state=100))])
cv score: [0.70458333 0.615 0.76375 0.81541667 0.79549051]
----------------------------------------
Trial 3929
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=135, random_state=100))])
cv score: [0.69583333 0.62666667 0.72083333 0.80916667 0.84731013]
----------------------------------------
Trial 3930
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=21,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.69416667 0.7475 0.67416667 0.78481013]
----------------------------------------
Trial 3931
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='sqrt',
n_estimators=92, random_state=100,
subsample=0.8))])
cv score: [0.52833333 0.56833333 0.60916667 0.63833333 0.8164557 ]
----------------------------------------
Trial 3932
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.69416667 0.77166667 0.83666667 0.90743671]
----------------------------------------
Trial 3933
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
n_estimators=27, random_state=100,
subsample=0.6))])
cv score: [0.62208333 0.7075 0.53666667 0.85916667 0.82001582]
----------------------------------------
Trial 3934
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=107,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53541667 0.65375 0.66083333 0.79416667 0.78876582]
----------------------------------------
Trial 3935
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66833333 0.65666667 0.70083333 0.72083333 0.76898734]
----------------------------------------
Trial 3936
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=182, random_state=100))])
cv score: [0.72458333 0.67333333 0.77791667 0.8375 0.82120253]
----------------------------------------
Trial 3937
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=149,
random_state=100))])
cv score: [0.6925 0.50125 0.72375 0.64958333 0.63212025]
----------------------------------------
Trial 3938
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=106,
random_state=100))])
cv score: [0.6475 0.66333333 0.69583333 0.8075 0.84335443]
----------------------------------------
Trial 3939
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=198,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59833333 0.63 0.755 0.67333333 0.76582278]
----------------------------------------
Trial 3940
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.61916667 0.7775 0.7975 0.81170886]
----------------------------------------
Trial 3941
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='log2',
n_estimators=19,
random_state=100))])
cv score: [0.6125 0.65041667 0.635 0.82 0.77373418]
----------------------------------------
Trial 3942
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61583333 0.53833333 0.73666667 0.65 0.76503165]
----------------------------------------
Trial 3943
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
n_estimators=179, random_state=100,
subsample=0.9))])
cv score: [0.74083333 0.65875 0.81 0.8325 0.75870253]
----------------------------------------
Trial 3944
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='log2',
n_estimators=180, random_state=100,
subsample=0.9))])
cv score: [0.60916667 0.575 0.66916667 0.7125 0.76344937]
----------------------------------------
Trial 3945
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=104,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65416667 0.5675 0.73833333 0.69166667 0.74762658]
----------------------------------------
Trial 3946
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=156, random_state=100))])
cv score: [0.715 0.67791667 0.77916667 0.84166667 0.84098101]
----------------------------------------
Trial 3947
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=45,
random_state=100))])
cv score: [0.66458333 0.50541667 0.7975 0.66166667 0.78876582]
----------------------------------------
Trial 3948
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=18,
random_state=100))])
cv score: [0.65041667 0.60208333 0.73416667 0.8025 0.78441456]
----------------------------------------
Trial 3949
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=80,
random_state=100))])
cv score: [0.78916667 0.63375 0.76875 0.83875 0.85087025]
----------------------------------------
Trial 3950
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=111,
random_state=100))])
cv score: [0.76458333 0.49375 0.78791667 0.70208333 0.79232595]
----------------------------------------
Trial 3951
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.69 0.77 0.79583333 0.91297468]
----------------------------------------
Trial 3952
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=108, random_state=100))])
cv score: [0.735 0.68708333 0.72416667 0.85833333 0.87420886]
----------------------------------------
Trial 3953
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61833333 0.69416667 0.78166667 0.7525 0.8306962 ]
----------------------------------------
Trial 3954
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=127, random_state=100,
subsample=0.9))])
cv score: [0.72166667 0.60916667 0.72666667 0.81916667 0.81170886]
----------------------------------------
Trial 3955
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=178, random_state=100,
subsample=0.7))])
cv score: [0.5725 0.6525 0.675 0.775 0.77294304]
----------------------------------------
Trial 3956
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=81, random_state=100,
subsample=0.8))])
cv score: [0.70583333 0.59833333 0.70666667 0.80416667 0.80063291]
----------------------------------------
Trial 3957
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='sqrt',
n_estimators=92, random_state=100,
subsample=0.75))])
cv score: [0.67916667 0.5925 0.73916667 0.8175 0.76107595]
----------------------------------------
Trial 3958
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=63, random_state=100,
subsample=0.7))])
cv score: [0.47166667 0.6325 0.6875 0.71833333 0.7721519 ]
----------------------------------------
Trial 3959
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.69416667 0.79416667 0.82833333 0.89556962]
----------------------------------------
Trial 3960
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65666667 0.645 0.74333333 0.7175 0.78718354]
----------------------------------------
Trial 3961
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71458333 0.72958333 0.76541667 0.84416667 0.8568038 ]
----------------------------------------
Trial 3962
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=135,
random_state=100))])
cv score: [0.68708333 0.49375 0.80875 0.64625 0.75316456]
----------------------------------------
Trial 3963
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71333333 0.70333333 0.7925 0.78916667 0.86392405]
----------------------------------------
Trial 3964
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=114, random_state=100,
subsample=0.8))])
cv score: [0.72166667 0.6075 0.7425 0.8375 0.80221519]
----------------------------------------
Trial 3965
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=143,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61416667 0.6475 0.7625 0.73 0.77927215]
----------------------------------------
Trial 3966
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=27, random_state=100,
subsample=0.6))])
cv score: [0.68333333 0.6725 0.76 0.82583333 0.86234177]
----------------------------------------
Trial 3967
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=128,
random_state=100))])
cv score: [0.64333333 0.57833333 0.74083333 0.78333333 0.77294304]
----------------------------------------
Trial 3968
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=112,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79083333 0.70833333 0.77875 0.86166667 0.88844937]
----------------------------------------
Trial 3969
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=146,
random_state=100))])
cv score: [0.71666667 0.59041667 0.755 0.76333333 0.6784019 ]
----------------------------------------
Trial 3970
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=119, random_state=100,
subsample=0.7))])
cv score: [0.70916667 0.6775 0.75916667 0.80916667 0.81408228]
----------------------------------------
Trial 3971
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=191,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66666667 0.67666667 0.7625 0.78583333 0.82753165]
----------------------------------------
Trial 3972
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=142, random_state=100))])
cv score: [0.75916667 0.67791667 0.78583333 0.835 0.84493671]
----------------------------------------
Trial 3973
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=137, random_state=100,
subsample=0.9))])
cv score: [0.64583333 0.53666667 0.65833333 0.74416667 0.77056962]
----------------------------------------
Trial 3974
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=155, random_state=100))])
cv score: [0.63625 0.69083333 0.6775 0.81833333 0.82713608]
----------------------------------------
Trial 3975
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='sqrt',
n_estimators=125, random_state=100,
subsample=0.95))])
cv score: [0.55291667 0.67416667 0.66625 0.78958333 0.78362342]
----------------------------------------
Trial 3976
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.7))])
cv score: [0.64583333 0.64083333 0.7075 0.78583333 0.84335443]
----------------------------------------
Trial 3977
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=93,
random_state=100))])
cv score: [0.56125 0.66208333 0.69625 0.82791667 0.79628165]
----------------------------------------
Trial 3978
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=92,
random_state=100))])
cv score: [0.67166667 0.59666667 0.67666667 0.80833333 0.83623418]
----------------------------------------
Trial 3979
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=94, random_state=100,
subsample=0.7))])
cv score: [0.73 0.61166667 0.71833333 0.8225 0.79588608]
----------------------------------------
Trial 3980
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
n_estimators=187, random_state=100,
subsample=0.9))])
cv score: [0.60333333 0.62416667 0.76583333 0.8225 0.77768987]
----------------------------------------
Trial 3981
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=22,
random_state=100))])
cv score: [0.58083333 0.65916667 0.65708333 0.82958333 0.82871835]
----------------------------------------
Trial 3982
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=159,
random_state=100))])
cv score: [0.76458333 0.49375 0.78708333 0.70208333 0.79232595]
----------------------------------------
Trial 3983
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=101, random_state=100,
subsample=0.7))])
cv score: [0.65416667 0.62083333 0.71583333 0.81416667 0.77689873]
----------------------------------------
Trial 3984
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=174, random_state=100,
subsample=0.9))])
cv score: [0.76083333 0.62833333 0.81166667 0.835 0.7943038 ]
----------------------------------------
Trial 3985
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
n_estimators=71, random_state=100,
subsample=0.85))])
cv score: [0.70416667 0.63333333 0.7625 0.79666667 0.79746835]
----------------------------------------
Trial 3986
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=94,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63333333 0.6725 0.78583333 0.77666667 0.79786392]
----------------------------------------
Trial 3987
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=34,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.555 0.57 0.76166667 0.73833333 0.76503165]
----------------------------------------
Trial 3988
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=25,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63833333 0.74916667 0.79375 0.84333333 0.82041139]
----------------------------------------
Trial 3989
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='sqrt',
n_estimators=53, random_state=100,
subsample=0.95))])
cv score: [0.62416667 0.54 0.735 0.76833333 0.66851266]
----------------------------------------
Trial 3990
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=65,
random_state=100))])
cv score: [0.69166667 0.4875 0.72125 0.67166667 0.63607595]
----------------------------------------
Trial 3991
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=12,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79833333 0.71458333 0.795 0.80166667 0.90664557]
----------------------------------------
Trial 3992
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=184, random_state=100,
subsample=0.65))])
cv score: [0.62833333 0.5875 0.6625 0.72 0.69778481]
----------------------------------------
Trial 3993
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=22, random_state=100,
subsample=0.9))])
cv score: [0.63916667 0.61833333 0.645 0.7725 0.79667722]
----------------------------------------
Trial 3994
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=122,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.735 0.72708333 0.78208333 0.84 0.86867089]
----------------------------------------
Trial 3995
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.71083333 0.78416667 0.79166667 0.8568038 ]
----------------------------------------
Trial 3996
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
n_estimators=159, random_state=100,
subsample=0.65))])
cv score: [0.71333333 0.66333333 0.76 0.80166667 0.80696203]
----------------------------------------
Trial 3997
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=50, random_state=100))])
cv score: [0.68333333 0.68125 0.715 0.7625 0.88053797]
----------------------------------------
Trial 3998
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='sqrt',
n_estimators=49, random_state=100,
subsample=0.9))])
cv score: [0.6675 0.60583333 0.67916667 0.77166667 0.78006329]
----------------------------------------
Trial 3999
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=87,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71333333 0.70916667 0.77583333 0.8125 0.8789557 ]
----------------------------------------
Trial 4000
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=145,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 4001
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=151,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.68833333 0.75166667 0.805 0.88528481]
----------------------------------------
Trial 4002
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=103, random_state=100))])
cv score: [0.6925 0.64416667 0.7325 0.81166667 0.8306962 ]
----------------------------------------
Trial 4003
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.695 0.715 0.73791667 0.84833333 0.87341772]
----------------------------------------
Trial 4004
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='sqrt',
n_estimators=174, random_state=100,
subsample=0.8))])
cv score: [0.69 0.69 0.72666667 0.7875 0.74367089]
----------------------------------------
Trial 4005
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=106,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 4006
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64916667 0.71583333 0.69833333 0.685 0.66297468]
----------------------------------------
Trial 4007
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=136,
random_state=100))])
cv score: [0.69083333 0.62416667 0.68916667 0.795 0.81724684]
----------------------------------------
Trial 4008
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.72333333 0.7575 0.8625 0.8971519 ]
----------------------------------------
Trial 4009
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=193,
random_state=100))])
cv score: [0.645 0.57833333 0.69 0.7725 0.78481013]
----------------------------------------
Trial 4010
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=55, random_state=100))])
cv score: [0.66833333 0.63916667 0.74666667 0.79166667 0.84018987]
----------------------------------------
Trial 4011
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=57,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.69166667 0.7875 0.81666667 0.88686709]
----------------------------------------
Trial 4012
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=12,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.6775 0.76875 0.80083333 0.88528481]
----------------------------------------
Trial 4013
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=10, random_state=100,
subsample=0.6))])
cv score: [0.56333333 0.605 0.52541667 0.71875 0.47626582]
----------------------------------------
Trial 4014
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='log2',
n_estimators=172, random_state=100,
subsample=0.9))])
cv score: [0.55583333 0.55083333 0.60833333 0.69666667 0.72151899]
----------------------------------------
Trial 4015
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
n_estimators=155, random_state=100,
subsample=0.8))])
cv score: [0.59916667 0.58166667 0.64416667 0.735 0.74287975]
----------------------------------------
Trial 4016
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=59, random_state=100))])
cv score: [0.74291667 0.67958333 0.775 0.82958333 0.85126582]
----------------------------------------
Trial 4017
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65583333 0.6175 0.80916667 0.73083333 0.80300633]
----------------------------------------
Trial 4018
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=170,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.695 0.645 0.76166667 0.75333333 0.8346519 ]
----------------------------------------
Trial 4019
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='sqrt',
n_estimators=58, random_state=100,
subsample=0.9))])
cv score: [0.63583333 0.61 0.68416667 0.75333333 0.77294304]
----------------------------------------
Trial 4020
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=141,
random_state=100, subsample=0.7))])
cv score: [0.68833333 0.63833333 0.75666667 0.81583333 0.82041139]
----------------------------------------
Trial 4021
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=14, random_state=100,
subsample=0.7))])
cv score: [0.64166667 0.72625 0.65625 0.81583333 0.84018987]
----------------------------------------
Trial 4022
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=182,
random_state=100))])
cv score: [0.59 0.66416667 0.70083333 0.82833333 0.8306962 ]
----------------------------------------
Trial 4023
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=162,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75333333 0.7125 0.79083333 0.85083333 0.91613924]
----------------------------------------
Trial 4024
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=24, random_state=100,
subsample=0.95))])
cv score: [0.64416667 0.60625 0.72041667 0.825 0.77927215]
----------------------------------------
Trial 4025
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=103, random_state=100))])
cv score: [0.6775 0.635 0.7225 0.80083333 0.84098101]
----------------------------------------
Trial 4026
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=27,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.71375 0.78583333 0.85 0.89240506]
----------------------------------------
Trial 4027
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=126,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61 0.60583333 0.75583333 0.71083333 0.75672468]
----------------------------------------
Trial 4028
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='sqrt',
n_estimators=169, random_state=100,
subsample=0.75))])
cv score: [0.58833333 0.65416667 0.6825 0.775 0.80617089]
----------------------------------------
Trial 4029
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=134,
random_state=100))])
cv score: [0.6675 0.59 0.70166667 0.81 0.82199367]
----------------------------------------
Trial 4030
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=184,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7425 0.67166667 0.7925 0.79833333 0.89161392]
----------------------------------------
Trial 4031
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=85, random_state=100,
subsample=0.65))])
cv score: [0.5875 0.52583333 0.57416667 0.65 0.65348101]
----------------------------------------
Trial 4032
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6025 0.64083333 0.73833333 0.77 0.80933544]
----------------------------------------
Trial 4033
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=198, random_state=100))])
cv score: [0.695 0.63916667 0.72083333 0.80583333 0.84414557]
----------------------------------------
Trial 4034
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=104, random_state=100))])
cv score: [0.72666667 0.67583333 0.78166667 0.83583333 0.83623418]
----------------------------------------
Trial 4035
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
n_estimators=15,
random_state=100))])
cv score: [0.74166667 0.60833333 0.78958333 0.84333333 0.81210443]
----------------------------------------
Trial 4036
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.8))])
cv score: [0.6775 0.67791667 0.5775 0.84083333 0.84968354]
----------------------------------------
Trial 4037
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=42, random_state=100,
subsample=0.95))])
cv score: [0.78875 0.62958333 0.80541667 0.8275 0.8306962 ]
----------------------------------------
Trial 4038
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=172,
random_state=100))])
cv score: [0.7525 0.60833333 0.78166667 0.8375 0.82199367]
----------------------------------------
Trial 4039
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=84, random_state=100))])
cv score: [0.70083333 0.6675 0.72 0.79083333 0.86629747]
----------------------------------------
Trial 4040
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=136,
random_state=100, subsample=0.7))])
cv score: [0.66833333 0.65666667 0.77083333 0.8025 0.8306962 ]
----------------------------------------
Trial 4041
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=129, random_state=100,
subsample=0.6))])
cv score: [0.695 0.645 0.69666667 0.77916667 0.79746835]
----------------------------------------
Trial 4042
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=177,
random_state=100))])
cv score: [0.66541667 0.58666667 0.71958333 0.80875 0.79984177]
----------------------------------------
Trial 4043
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=99,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 4044
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, n_estimators=105,
random_state=100, subsample=0.8))])
cv score: [0.73333333 0.6375 0.745 0.81333333 0.80537975]
----------------------------------------
Trial 4045
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=69, random_state=100))])
cv score: [0.63916667 0.66166667 0.74166667 0.7975 0.82278481]
----------------------------------------
Trial 4046
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=76, random_state=100,
subsample=0.95))])
cv score: [0.55083333 0.555 0.69166667 0.67 0.73338608]
----------------------------------------
Trial 4047
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7825 0.73583333 0.7875 0.835 0.8710443 ]
----------------------------------------
Trial 4048
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=41,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.745 0.72583333 0.7975 0.80833333 0.84968354]
----------------------------------------
Trial 4049
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=123,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.7075 0.75916667 0.84916667 0.86867089]
----------------------------------------
Trial 4050
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=87, random_state=100))])
cv score: [0.73333333 0.69541667 0.72583333 0.85166667 0.87816456]
----------------------------------------
Trial 4051
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=106,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63666667 0.68666667 0.7425 0.7575 0.73971519]
----------------------------------------
Trial 4052
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60666667 0.6225 0.71333333 0.73666667 0.79509494]
----------------------------------------
Trial 4053
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=166, random_state=100))])
cv score: [0.57583333 0.70083333 0.705 0.81666667 0.81566456]
----------------------------------------
Trial 4054
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=34, random_state=100))])
cv score: [0.67583333 0.61083333 0.74583333 0.81541667 0.84256329]
----------------------------------------
Trial 4055
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=54, random_state=100,
subsample=0.9))])
cv score: [0.7475 0.61833333 0.81291667 0.8275 0.7278481 ]
----------------------------------------
Trial 4056
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.745 0.71833333 0.79 0.86416667 0.87816456]
----------------------------------------
Trial 4057
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77 0.7475 0.76833333 0.86666667 0.86234177]
----------------------------------------
Trial 4058
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
n_estimators=60, random_state=100,
subsample=0.6))])
cv score: [0.7125 0.68333333 0.76666667 0.815 0.81724684]
----------------------------------------
Trial 4059
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=167,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 4060
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61666667 0.6425 0.73583333 0.6 0.68117089]
----------------------------------------
Trial 4061
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=29,
random_state=100))])
cv score: [0.65791667 0.60416667 0.76375 0.77791667 0.81526899]
----------------------------------------
Trial 4062
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.72333333 0.7675 0.86083333 0.86787975]
----------------------------------------
Trial 4063
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.595 0.59583333 0.74083333 0.72916667 0.77373418]
----------------------------------------
Trial 4064
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=179,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68083333 0.66666667 0.77 0.8 0.87658228]
----------------------------------------
Trial 4065
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66416667 0.63833333 0.77833333 0.76666667 0.8568038 ]
----------------------------------------
Trial 4066
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
n_estimators=84, random_state=100,
subsample=0.75))])
cv score: [0.69916667 0.66 0.775 0.81916667 0.80300633]
----------------------------------------
Trial 4067
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='log2',
n_estimators=185, random_state=100,
subsample=0.75))])
cv score: [0.61583333 0.58333333 0.655 0.67333333 0.6875 ]
----------------------------------------
Trial 4068
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=186,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64 0.62916667 0.79 0.72833333 0.76186709]
----------------------------------------
Trial 4069
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=168,
random_state=100))])
cv score: [0.56708333 0.66291667 0.70333333 0.82375 0.8164557 ]
----------------------------------------
Trial 4070
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='sqrt',
n_estimators=157, random_state=100,
subsample=0.6))])
cv score: [0.70833333 0.64583333 0.73833333 0.81333333 0.81408228]
----------------------------------------
Trial 4071
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=102, random_state=100))])
cv score: [0.74583333 0.67916667 0.78166667 0.83583333 0.84810127]
----------------------------------------
Trial 4072
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=18, random_state=100))])
cv score: [0.73208333 0.69041667 0.71916667 0.86708333 0.88330696]
----------------------------------------
Trial 4073
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=113,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7875 0.71625 0.77875 0.85 0.85522152]
----------------------------------------
Trial 4074
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(n_estimators=97, random_state=100,
subsample=0.8))])
cv score: [0.73833333 0.69916667 0.76333333 0.79 0.82357595]
----------------------------------------
Trial 4075
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71 0.7125 0.75666667 0.85666667 0.87025316]
----------------------------------------
Trial 4076
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='log2',
n_estimators=50, random_state=100,
subsample=0.75))])
cv score: [0.57833333 0.64916667 0.68416667 0.6575 0.75791139]
----------------------------------------
Trial 4077
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=83, random_state=100))])
cv score: [0.56083333 0.73208333 0.68916667 0.79166667 0.80063291]
----------------------------------------
Trial 4078
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=112,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6825 0.60916667 0.76833333 0.73916667 0.79272152]
----------------------------------------
Trial 4079
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=57, random_state=100,
subsample=0.95))])
cv score: [0.66416667 0.58666667 0.70416667 0.8025 0.78243671]
----------------------------------------
Trial 4080
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=119, random_state=100,
subsample=0.75))])
cv score: [0.62833333 0.62333333 0.61666667 0.7025 0.67800633]
----------------------------------------
Trial 4081
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=147,
random_state=100))])
cv score: [0.6875 0.5975 0.69833333 0.7925 0.81012658]
----------------------------------------
Trial 4082
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=125, random_state=100,
subsample=0.6))])
cv score: [0.61416667 0.61166667 0.68333333 0.6675 0.61867089]
----------------------------------------
Trial 4083
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=189,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.615 0.62416667 0.73583333 0.695 0.78401899]
----------------------------------------
Trial 4084
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=33,
random_state=100))])
cv score: [0.67 0.59708333 0.69166667 0.78166667 0.82041139]
----------------------------------------
Trial 4085
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=140,
random_state=100))])
cv score: [0.73125 0.56958333 0.78333333 0.8075 0.80181962]
----------------------------------------
Trial 4086
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6175 0.545 0.715 0.70583333 0.75158228]
----------------------------------------
Trial 4087
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=82,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67875 0.70875 0.76 0.85583333 0.84256329]
----------------------------------------
Trial 4088
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=76,
random_state=100))])
cv score: [0.52541667 0.65458333 0.71916667 0.8225 0.76186709]
----------------------------------------
Trial 4089
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=30,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77833333 0.70833333 0.80541667 0.85083333 0.90348101]
----------------------------------------
Trial 4090
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74416667 0.7125 0.81333333 0.8375 0.88370253]
----------------------------------------
Trial 4091
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=186,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79916667 0.66291667 0.78876582]
----------------------------------------
Trial 4092
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=179, random_state=100,
subsample=0.85))])
cv score: [0.6475 0.66333333 0.71166667 0.76333333 0.65031646]
----------------------------------------
Trial 4093
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=99, random_state=100,
subsample=0.8))])
cv score: [0.55083333 0.6875 0.68916667 0.74166667 0.63291139]
----------------------------------------
Trial 4094
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=143,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.635 0.66166667 0.71166667 0.8125 0.82199367]
----------------------------------------
Trial 4095
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=137,
random_state=100))])
cv score: [0.68708333 0.49375 0.80875 0.64625 0.75316456]
----------------------------------------
Trial 4096
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=167, random_state=100))])
cv score: [0.57666667 0.70166667 0.705 0.8175 0.81566456]
----------------------------------------
Trial 4097
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=128, random_state=100,
subsample=0.8))])
cv score: [0.7375 0.68333333 0.78416667 0.8375 0.80300633]
----------------------------------------
Trial 4098
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=141, random_state=100))])
cv score: [0.68166667 0.6225 0.75 0.81916667 0.82594937]
----------------------------------------
Trial 4099
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=10,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65416667 0.62833333 0.69583333 0.68 0.8164557 ]
----------------------------------------
Trial 4100
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=75,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66791667 0.72416667 0.75 0.86791667 0.83781646]
----------------------------------------
Trial 4101
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=160,
random_state=100))])
cv score: [0.70166667 0.60333333 0.67666667 0.80416667 0.84493671]
----------------------------------------
Trial 4102
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=161, random_state=100,
subsample=0.95))])
cv score: [0.60916667 0.53833333 0.65833333 0.76666667 0.73338608]
----------------------------------------
Trial 4103
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
n_estimators=121, random_state=100,
subsample=0.75))])
cv score: [0.70416667 0.6675 0.77166667 0.82416667 0.83227848]
----------------------------------------
Trial 4104
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=22,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66875 0.74791667 0.78041667 0.82916667 0.86313291]
----------------------------------------
Trial 4105
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.66833333 0.7875 0.79833333 0.88924051]
----------------------------------------
Trial 4106
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=120, random_state=100))])
cv score: [0.6575 0.62 0.73916667 0.80583333 0.84256329]
----------------------------------------
Trial 4107
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=127,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5625 0.62583333 0.74166667 0.74416667 0.82199367]
----------------------------------------
Trial 4108
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=101, random_state=100))])
cv score: [0.68458333 0.64208333 0.7275 0.82875 0.81882911]
----------------------------------------
Trial 4109
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=67,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.755 0.73041667 0.76208333 0.85416667 0.84651899]
----------------------------------------
Trial 4110
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=76, random_state=100))])
cv score: [0.65666667 0.63166667 0.75833333 0.80333333 0.82515823]
----------------------------------------
Trial 4111
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68458333 0.71791667 0.7725 0.8525 0.84968354]
----------------------------------------
Trial 4112
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=44, random_state=100,
subsample=0.6))])
cv score: [0.72416667 0.69416667 0.79416667 0.82416667 0.84810127]
----------------------------------------
Trial 4113
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=193, random_state=100))])
cv score: [0.68666667 0.63416667 0.72083333 0.79666667 0.83860759]
----------------------------------------
Trial 4114
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=157,
random_state=100))])
cv score: [0.75291667 0.60833333 0.78166667 0.8375 0.82199367]
----------------------------------------
Trial 4115
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5925 0.71166667 0.77416667 0.6775 0.71756329]
----------------------------------------
Trial 4116
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.72541667 0.75958333 0.8475 0.87420886]
----------------------------------------
Trial 4117
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=51,
random_state=100))])
cv score: [0.69666667 0.65458333 0.70833333 0.77833333 0.84889241]
----------------------------------------
Trial 4118
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=19, random_state=100,
subsample=0.65))])
cv score: [0.56916667 0.65083333 0.69333333 0.74583333 0.75 ]
----------------------------------------
Trial 4119
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='log2',
n_estimators=164, random_state=100,
subsample=0.8))])
cv score: [0.66166667 0.59666667 0.6975 0.77083333 0.7539557 ]
----------------------------------------
Trial 4120
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=41, random_state=100,
subsample=0.95))])
cv score: [0.63416667 0.665 0.72333333 0.78625 0.81012658]
----------------------------------------
Trial 4121
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=178,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.80083333 0.73083333 0.785 0.84083333 0.87341772]
----------------------------------------
Trial 4122
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=10,
random_state=100))])
cv score: [0.6525 0.60208333 0.74916667 0.78375 0.80181962]
----------------------------------------
Trial 4123
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=71,
random_state=100))])
cv score: [0.65416667 0.66583333 0.72083333 0.81666667 0.84414557]
----------------------------------------
Trial 4124
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=167,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7875 0.73333333 0.78083333 0.84666667 0.875 ]
----------------------------------------
Trial 4125
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
n_estimators=124, random_state=100,
subsample=0.65))])
cv score: [0.65333333 0.56666667 0.69083333 0.55 0.60363924]
----------------------------------------
Trial 4126
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=83,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71916667 0.72291667 0.7625 0.85166667 0.86787975]
----------------------------------------
Trial 4127
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=63, random_state=100,
subsample=0.75))])
cv score: [0.67583333 0.67083333 0.68 0.82083333 0.83386076]
----------------------------------------
Trial 4128
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=141, random_state=100))])
cv score: [0.77583333 0.67833333 0.75333333 0.85083333 0.90031646]
----------------------------------------
Trial 4129
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=191, random_state=100))])
cv score: [0.69083333 0.61 0.7325 0.8225 0.82120253]
----------------------------------------
Trial 4130
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=154,
random_state=100,
subsample=0.75))])
cv score: [0.65833333 0.65166667 0.75083333 0.80333333 0.82832278]
----------------------------------------
Trial 4131
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=155,
random_state=100))])
cv score: [0.75041667 0.58791667 0.80833333 0.79208333 0.80458861]
----------------------------------------
Trial 4132
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=185, random_state=100))])
cv score: [0.6675 0.65666667 0.7175 0.80916667 0.85363924]
----------------------------------------
Trial 4133
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=143,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75 0.68166667 0.77333333 0.78833333 0.88528481]
----------------------------------------
Trial 4134
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76833333 0.68916667 0.77083333 0.755 0.87579114]
----------------------------------------
Trial 4135
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=51,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77666667 0.72 0.78083333 0.84666667 0.90110759]
----------------------------------------
Trial 4136
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
n_estimators=178, random_state=100,
subsample=0.7))])
cv score: [0.5725 0.6225 0.7225 0.78333333 0.75791139]
----------------------------------------
Trial 4137
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64083333 0.645 0.76833333 0.76416667 0.76344937]
----------------------------------------
Trial 4138
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=113,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71333333 0.66 0.78 0.785 0.84572785]
----------------------------------------
Trial 4139
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=88,
random_state=100))])
cv score: [0.69666667 0.57916667 0.69833333 0.785 0.79984177]
----------------------------------------
Trial 4140
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=197, random_state=100,
subsample=0.9))])
cv score: [0.67916667 0.58833333 0.70666667 0.7925 0.83939873]
----------------------------------------
Trial 4141
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=30,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.67642405]
----------------------------------------
Trial 4142
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=102,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75833333 0.72583333 0.81083333 0.83416667 0.86550633]
----------------------------------------
Trial 4143
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=123,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64 0.61166667 0.76166667 0.68916667 0.84572785]
----------------------------------------
Trial 4144
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=144,
random_state=100))])
cv score: [0.7 0.6025 0.68083333 0.80333333 0.84256329]
----------------------------------------
Trial 4145
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.745 0.70666667 0.78583333 0.845 0.9153481 ]
----------------------------------------
Trial 4146
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=152, random_state=100))])
cv score: [0.6925 0.6275 0.72333333 0.805 0.84889241]
----------------------------------------
Trial 4147
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=62,
random_state=100))])
cv score: [0.7525 0.58791667 0.80833333 0.79416667 0.80458861]
----------------------------------------
Trial 4148
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.67583333 0.785 0.78833333 0.87974684]
----------------------------------------
Trial 4149
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=111,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76833333 0.73 0.79833333 0.83583333 0.88686709]
----------------------------------------
Trial 4150
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=105, random_state=100,
subsample=0.7))])
cv score: [0.67416667 0.62333333 0.74166667 0.80333333 0.84493671]
----------------------------------------
Trial 4151
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=172,
random_state=100))])
cv score: [0.6825 0.58083333 0.72083333 0.81666667 0.83148734]
----------------------------------------
Trial 4152
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=52,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66583333 0.63666667 0.79416667 0.765 0.81408228]
----------------------------------------
Trial 4153
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=25,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68791667 0.73666667 0.76916667 0.84666667 0.89280063]
----------------------------------------
Trial 4154
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=64,
random_state=100))])
cv score: [0.60333333 0.67375 0.69333333 0.80916667 0.83781646]
----------------------------------------
Trial 4155
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.65))])
cv score: [0.685 0.63416667 0.73916667 0.8125 0.83306962]
----------------------------------------
Trial 4156
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=165,
random_state=100))])
cv score: [0.69416667 0.51791667 0.79833333 0.66875 0.62935127]
----------------------------------------
Trial 4157
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=192,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.655 0.77916667 0.77166667 0.86075949]
----------------------------------------
Trial 4158
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=152,
random_state=100))])
cv score: [0.58416667 0.66333333 0.71083333 0.83333333 0.82753165]
----------------------------------------
Trial 4159
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=44, random_state=100))])
cv score: [0.67666667 0.62166667 0.74833333 0.81916667 0.83148734]
----------------------------------------
Trial 4160
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='log2',
n_estimators=106, random_state=100,
subsample=0.8))])
cv score: [0.685 0.63166667 0.71166667 0.80166667 0.80617089]
----------------------------------------
Trial 4161
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.68416667 0.76166667 0.81583333 0.89794304]
----------------------------------------
Trial 4162
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='log2',
n_estimators=20, random_state=100,
subsample=0.75))])
cv score: [0.6 0.62666667 0.70083333 0.79833333 0.76740506]
----------------------------------------
Trial 4163
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='log2',
n_estimators=122, random_state=100,
subsample=0.7))])
cv score: [0.64083333 0.70791667 0.67416667 0.79833333 0.83623418]
----------------------------------------
Trial 4164
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7725 0.715 0.7875 0.83833333 0.87974684]
----------------------------------------
Trial 4165
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65666667 0.755 0.79166667 0.865 0.84335443]
----------------------------------------
Trial 4166
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70375 0.735 0.7725 0.82208333 0.91376582]
----------------------------------------
Trial 4167
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='log2',
n_estimators=109,
random_state=100))])
cv score: [0.6275 0.70875 0.72666667 0.82041667 0.83544304]
----------------------------------------
Trial 4168
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='log2',
n_estimators=190,
random_state=100))])
cv score: [0.66416667 0.59416667 0.71333333 0.7925 0.76265823]
----------------------------------------
Trial 4169
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=109, random_state=100))])
cv score: [0.68583333 0.65833333 0.72916667 0.80416667 0.85838608]
----------------------------------------
Trial 4170
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.73541667 0.77166667 0.86833333 0.8306962 ]
----------------------------------------
Trial 4171
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=42, random_state=100))])
cv score: [0.64416667 0.67083333 0.71083333 0.79166667 0.82436709]
----------------------------------------
Trial 4172
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=95,
random_state=100))])
cv score: [0.6825 0.57 0.68083333 0.76416667 0.7460443 ]
----------------------------------------
Trial 4173
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72333333 0.72916667 0.78833333 0.85 0.90110759]
----------------------------------------
Trial 4174
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=115,
random_state=100, subsample=0.6))])
cv score: [0.66916667 0.65833333 0.765 0.82083333 0.79272152]
----------------------------------------
Trial 4175
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.755 0.79 0.84833333 0.875 ]
----------------------------------------
Trial 4176
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=104,
random_state=100))])
cv score: [0.6775 0.58666667 0.68333333 0.81083333 0.8306962 ]
----------------------------------------
Trial 4177
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=67,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64666667 0.64916667 0.71583333 0.75333333 0.80142405]
----------------------------------------
Trial 4178
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=121,
random_state=100))])
cv score: [0.65666667 0.67083333 0.70166667 0.81 0.84493671]
----------------------------------------
Trial 4179
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=104, random_state=100))])
cv score: [0.72375 0.67 0.78166667 0.83583333 0.83267405]
----------------------------------------
Trial 4180
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, n_estimators=156,
random_state=100, subsample=0.7))])
cv score: [0.68416667 0.6625 0.775 0.8225 0.82120253]
----------------------------------------
Trial 4181
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=133,
random_state=100))])
cv score: [0.68083333 0.58583333 0.69666667 0.79166667 0.80300633]
----------------------------------------
Trial 4182
----------------------------------------
Parameters {'xgb__n_estimators': 26, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=26,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66583333 0.71625 0.73458333 0.84333333 0.87420886]
----------------------------------------
Trial 4183
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='log2',
n_estimators=84, random_state=100,
subsample=0.8))])
cv score: [0.46166667 0.6325 0.65166667 0.6375 0.78243671]
----------------------------------------
Trial 4184
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=189,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69583333 0.67166667 0.79 0.7775 0.84098101]
----------------------------------------
Trial 4185
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=198, random_state=100))])
cv score: [0.66333333 0.65166667 0.73583333 0.80416667 0.84810127]
----------------------------------------
Trial 4186
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60916667 0.65 0.79416667 0.73583333 0.79746835]
----------------------------------------
Trial 4187
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='sqrt',
n_estimators=151, random_state=100,
subsample=0.85))])
cv score: [0.66333333 0.64666667 0.68083333 0.76166667 0.78718354]
----------------------------------------
Trial 4188
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71166667 0.6675 0.79666667 0.80416667 0.87816456]
----------------------------------------
Trial 4189
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=170,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.6975 0.78583333 0.83 0.85047468]
----------------------------------------
Trial 4190
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='sqrt',
n_estimators=51, random_state=100,
subsample=0.65))])
cv score: [0.525 0.55833333 0.5925 0.7075 0.71360759]
----------------------------------------
Trial 4191
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68083333 0.585 0.77416667 0.73 0.81724684]
----------------------------------------
Trial 4192
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
n_estimators=99,
random_state=100))])
cv score: [0.72708333 0.52458333 0.78916667 0.77416667 0.78164557]
----------------------------------------
Trial 4193
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=188, random_state=100))])
cv score: [0.68416667 0.62208333 0.74166667 0.81333333 0.82080696]
----------------------------------------
Trial 4194
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=19,
random_state=100))])
cv score: [0.6975 0.54125 0.68333333 0.83458333 0.88528481]
----------------------------------------
Trial 4195
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=42, random_state=100,
subsample=0.85))])
cv score: [0.65916667 0.5725 0.70166667 0.75916667 0.7943038 ]
----------------------------------------
Trial 4196
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=146,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74666667 0.67833333 0.78333333 0.83166667 0.90506329]
----------------------------------------
Trial 4197
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
n_estimators=148, random_state=100,
subsample=0.85))])
cv score: [0.6275 0.5475 0.71416667 0.78166667 0.71439873]
----------------------------------------
Trial 4198
----------------------------------------
Parameters {'xgb__n_estimators': 115, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=115,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59416667 0.61583333 0.715 0.725 0.7943038 ]
----------------------------------------
Trial 4199
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.71333333 0.78916667 0.83916667 0.8931962 ]
----------------------------------------
Trial 4200
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=178,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57833333 0.60333333 0.71458333 0.74166667 0.76977848]
----------------------------------------
Trial 4201
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=113,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7375 0.7175 0.75291667 0.86 0.87025316]
----------------------------------------
Trial 4202
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=127, random_state=100))])
cv score: [0.63875 0.69833333 0.65625 0.8125 0.82120253]
----------------------------------------
Trial 4203
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=73,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83625 0.82199367]
----------------------------------------
Trial 4204
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=39, random_state=100,
subsample=0.85))])
cv score: [0.65333333 0.60583333 0.6625 0.755 0.75237342]
----------------------------------------
Trial 4205
----------------------------------------
Parameters {'xgb__n_estimators': 26, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=26,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7125 0.7175 0.785 0.85166667 0.89240506]
----------------------------------------
Trial 4206
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=124,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.68583333 0.8025 0.77333333 0.88291139]
----------------------------------------
Trial 4207
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=116,
random_state=100))])
cv score: [0.65583333 0.665 0.69916667 0.81083333 0.84098101]
----------------------------------------
Trial 4208
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
n_estimators=135, random_state=100,
subsample=0.6))])
cv score: [0.6075 0.645 0.72083333 0.78333333 0.6835443 ]
----------------------------------------
Trial 4209
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=27,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78833333 0.72583333 0.79791667 0.83833333 0.88449367]
----------------------------------------
Trial 4210
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=130, random_state=100,
subsample=0.8))])
cv score: [0.5675 0.70083333 0.69416667 0.7325 0.62341772]
----------------------------------------
Trial 4211
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
n_estimators=64, random_state=100,
subsample=0.85))])
cv score: [0.74333333 0.66416667 0.77333333 0.77083333 0.73417722]
----------------------------------------
Trial 4212
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.9))])
cv score: [0.65083333 0.58166667 0.7125 0.79083333 0.80379747]
----------------------------------------
Trial 4213
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=67,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68708333 0.68958333 0.69791667 0.85916667 0.86155063]
----------------------------------------
Trial 4214
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=175, random_state=100))])
cv score: [0.77166667 0.68791667 0.77833333 0.84083333 0.85126582]
----------------------------------------
Trial 4215
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=143, random_state=100,
subsample=0.75))])
cv score: [0.65166667 0.63708333 0.63083333 0.52083333 0.63132911]
----------------------------------------
Trial 4216
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=119,
random_state=100))])
cv score: [0.68666667 0.51375 0.82458333 0.66375 0.66574367]
----------------------------------------
Trial 4217
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.71583333 0.77416667 0.80833333 0.8971519 ]
----------------------------------------
Trial 4218
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
n_estimators=90, random_state=100,
subsample=0.9))])
cv score: [0.71416667 0.61166667 0.82291667 0.83166667 0.75079114]
----------------------------------------
Trial 4219
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=153, random_state=100))])
cv score: [0.77916667 0.6825 0.74666667 0.84916667 0.90348101]
----------------------------------------
Trial 4220
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71041667 0.74166667 0.78625 0.83583333 0.89082278]
----------------------------------------
Trial 4221
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=175, random_state=100,
subsample=0.8))])
cv score: [0.67916667 0.6325 0.76083333 0.75166667 0.68908228]
----------------------------------------
Trial 4222
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=77,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76416667 0.74333333 0.79125 0.8625 0.87183544]
----------------------------------------
Trial 4223
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=71,
random_state=100))])
cv score: [0.68458333 0.51375 0.82333333 0.66875 0.66574367]
----------------------------------------
Trial 4224
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=71, random_state=100))])
cv score: [0.61541667 0.69 0.63583333 0.825 0.81329114]
----------------------------------------
Trial 4225
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=183,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72416667 0.69916667 0.81 0.81583333 0.89398734]
----------------------------------------
Trial 4226
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=19, random_state=100))])
cv score: [0.67 0.6125 0.74 0.74083333 0.77927215]
----------------------------------------
Trial 4227
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=99,
random_state=100))])
cv score: [0.7 0.58083333 0.7 0.7875 0.80300633]
----------------------------------------
Trial 4228
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=104,
random_state=100))])
cv score: [0.5775 0.66416667 0.69416667 0.83083333 0.83702532]
----------------------------------------
Trial 4229
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=43, random_state=100,
subsample=0.65))])
cv score: [0.7325 0.68333333 0.77583333 0.83 0.78085443]
----------------------------------------
Trial 4230
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74041667 0.73416667 0.80916667 0.86166667 0.87974684]
----------------------------------------
Trial 4231
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=43,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 4232
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=81, random_state=100,
subsample=0.75))])
cv score: [0.74083333 0.67416667 0.8 0.8425 0.83860759]
----------------------------------------
Trial 4233
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=48,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6575 0.63916667 0.73333333 0.72666667 0.76503165]
----------------------------------------
Trial 4234
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71166667 0.70333333 0.78583333 0.82666667 0.90189873]
----------------------------------------
Trial 4235
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=54,
random_state=100))])
cv score: [0.56166667 0.67 0.68583333 0.82041667 0.84018987]
----------------------------------------
Trial 4236
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
n_estimators=32, random_state=100,
subsample=0.7))])
cv score: [0.71833333 0.65583333 0.80041667 0.81083333 0.82990506]
----------------------------------------
Trial 4237
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=87, random_state=100,
subsample=0.9))])
cv score: [0.6475 0.5375 0.68 0.77083333 0.78797468]
----------------------------------------
Trial 4238
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
n_estimators=33, random_state=100,
subsample=0.6))])
cv score: [0.69583333 0.70333333 0.78916667 0.79416667 0.80221519]
----------------------------------------
Trial 4239
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=68,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62666667 0.63083333 0.75583333 0.69916667 0.80696203]
----------------------------------------
Trial 4240
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='log2',
n_estimators=163, random_state=100,
subsample=0.85))])
cv score: [0.665 0.54166667 0.64916667 0.7225 0.6471519 ]
----------------------------------------
Trial 4241
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6725 0.5775 0.70666667 0.70166667 0.80696203]
----------------------------------------
Trial 4242
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=76, random_state=100))])
cv score: [0.6825 0.6475 0.73583333 0.81083333 0.82436709]
----------------------------------------
Trial 4243
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='sqrt',
n_estimators=78, random_state=100,
subsample=0.7))])
cv score: [0.61916667 0.67375 0.68291667 0.7775 0.80063291]
----------------------------------------
Trial 4244
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62333333 0.63416667 0.74916667 0.75916667 0.8085443 ]
----------------------------------------
Trial 4245
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=10,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76833333 0.65958333 0.73 0.74916667 0.81170886]
----------------------------------------
Trial 4246
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
n_estimators=189, random_state=100,
subsample=0.95))])
cv score: [0.67666667 0.59666667 0.67583333 0.73333333 0.7721519 ]
----------------------------------------
Trial 4247
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74416667 0.70833333 0.76 0.80166667 0.87420886]
----------------------------------------
Trial 4248
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=194, random_state=100))])
cv score: [0.67666667 0.62666667 0.72833333 0.80083333 0.83781646]
----------------------------------------
Trial 4249
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=10,
random_state=100))])
cv score: [0.75 0.58791667 0.80833333 0.79625 0.80379747]
----------------------------------------
Trial 4250
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='log2',
n_estimators=20, random_state=100,
subsample=0.85))])
cv score: [0.70416667 0.61416667 0.7125 0.79916667 0.6914557 ]
----------------------------------------
Trial 4251
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.67166667 0.78333333 0.80666667 0.87420886]
----------------------------------------
Trial 4252
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=125,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.52333333 0.63 0.78833333 0.66958333 0.72389241]
----------------------------------------
Trial 4253
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.71833333 0.79333333 0.805 0.85126582]
----------------------------------------
Trial 4254
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68916667 0.73125 0.80375 0.8475 0.89082278]
----------------------------------------
Trial 4255
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68916667 0.65833333 0.80083333 0.77166667 0.82357595]
----------------------------------------
Trial 4256
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=81, random_state=100,
subsample=0.7))])
cv score: [0.6025 0.67083333 0.67625 0.79 0.78481013]
----------------------------------------
Trial 4257
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=103,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75916667 0.73166667 0.77333333 0.8525 0.87420886]
----------------------------------------
Trial 4258
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='sqrt',
n_estimators=155, random_state=100,
subsample=0.65))])
cv score: [0.63166667 0.62583333 0.65 0.75833333 0.74129747]
----------------------------------------
Trial 4259
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=150, random_state=100,
subsample=0.7))])
cv score: [0.675 0.69666667 0.72833333 0.79833333 0.78481013]
----------------------------------------
Trial 4260
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=82, random_state=100))])
cv score: [0.73125 0.66833333 0.77958333 0.83458333 0.8346519 ]
----------------------------------------
Trial 4261
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70875 0.74208333 0.78125 0.8525 0.87460443]
----------------------------------------
Trial 4262
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=167, random_state=100,
subsample=0.65))])
cv score: [0.6725 0.615 0.70916667 0.79416667 0.83702532]
----------------------------------------
Trial 4263
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69208333 0.74583333 0.7725 0.84125 0.87183544]
----------------------------------------
Trial 4264
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=40,
random_state=100))])
cv score: [0.75583333 0.58791667 0.80833333 0.78666667 0.80379747]
----------------------------------------
Trial 4265
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=93, random_state=100))])
cv score: [0.60833333 0.70333333 0.7025 0.78916667 0.84177215]
----------------------------------------
Trial 4266
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=151,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 4267
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=183,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72166667 0.67833333 0.79 0.80416667 0.86155063]
----------------------------------------
Trial 4268
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=19,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.8225 0.74916667 0.7975 0.83833333 0.8971519 ]
----------------------------------------
Trial 4269
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=183,
random_state=100))])
cv score: [0.6925 0.4875 0.71958333 0.66875 0.63607595]
----------------------------------------
Trial 4270
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=121,
random_state=100))])
cv score: [0.76833333 0.66833333 0.73666667 0.82083333 0.80696203]
----------------------------------------
Trial 4271
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
random_state=100))])
cv score: [0.7 0.62583333 0.7225 0.80583333 0.82832278]
----------------------------------------
Trial 4272
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=80,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.685 0.78416667 0.83 0.90268987]
----------------------------------------
Trial 4273
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=173,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75541667 0.71833333 0.77625 0.83916667 0.86708861]
----------------------------------------
Trial 4274
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=150, random_state=100))])
cv score: [0.72875 0.67458333 0.7825 0.83208333 0.82911392]
----------------------------------------
Trial 4275
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71 0.65916667 0.78416667 0.78666667 0.86155063]
----------------------------------------
Trial 4276
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=146, random_state=100))])
cv score: [0.66166667 0.655 0.70416667 0.82083333 0.85838608]
----------------------------------------
Trial 4277
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=112,
random_state=100))])
cv score: [0.66833333 0.50541667 0.7975 0.66208333 0.78876582]
----------------------------------------
Trial 4278
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='log2',
n_estimators=106,
random_state=100))])
cv score: [0.68083333 0.5925 0.73666667 0.77833333 0.79509494]
----------------------------------------
Trial 4279
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=74,
random_state=100))])
cv score: [0.735 0.52291667 0.74458333 0.71708333 0.73971519]
----------------------------------------
Trial 4280
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=33, random_state=100))])
cv score: [0.62416667 0.73708333 0.70458333 0.82916667 0.87618671]
----------------------------------------
Trial 4281
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='sqrt',
n_estimators=106, random_state=100,
subsample=0.65))])
cv score: [0.65416667 0.5625 0.66166667 0.75666667 0.64794304]
----------------------------------------
Trial 4282
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=38, random_state=100))])
cv score: [0.77833333 0.68708333 0.75 0.845 0.89477848]
----------------------------------------
Trial 4283
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=178,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70166667 0.73041667 0.76125 0.85583333 0.84731013]
----------------------------------------
Trial 4284
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=48,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74083333 0.7125 0.81 0.78583333 0.89477848]
----------------------------------------
Trial 4285
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
random_state=100))])
cv score: [0.73166667 0.68291667 0.78083333 0.8325 0.84493671]
----------------------------------------
Trial 4286
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=185, random_state=100,
subsample=0.6))])
cv score: [0.6975 0.6225 0.715 0.76833333 0.84018987]
----------------------------------------
Trial 4287
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=186, random_state=100,
subsample=0.65))])
cv score: [0.5 0.5875 0.6775 0.70166667 0.75474684]
----------------------------------------
Trial 4288
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=75, random_state=100,
subsample=0.95))])
cv score: [0.64333333 0.63166667 0.735 0.81333333 0.73101266]
----------------------------------------
Trial 4289
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=71, random_state=100))])
cv score: [0.73958333 0.66708333 0.78666667 0.82958333 0.83939873]
----------------------------------------
Trial 4290
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65083333 0.63583333 0.735 0.7625 0.76977848]
----------------------------------------
Trial 4291
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=76,
random_state=100))])
cv score: [0.52541667 0.65458333 0.71916667 0.8225 0.76186709]
----------------------------------------
Trial 4292
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=51, random_state=100,
subsample=0.8))])
cv score: [0.69083333 0.67083333 0.79416667 0.84583333 0.80221519]
----------------------------------------
Trial 4293
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='sqrt',
n_estimators=165,
random_state=100))])
cv score: [0.68166667 0.62166667 0.665 0.76916667 0.68908228]
----------------------------------------
Trial 4294
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=137,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71666667 0.73166667 0.76958333 0.84416667 0.86313291]
----------------------------------------
Trial 4295
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=196,
random_state=100))])
cv score: [0.68583333 0.62333333 0.69833333 0.80333333 0.85205696]
----------------------------------------
Trial 4296
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=92, random_state=100))])
cv score: [0.71583333 0.6725 0.77583333 0.83708333 0.83939873]
----------------------------------------
Trial 4297
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=38,
random_state=100))])
cv score: [0.59458333 0.68291667 0.72541667 0.83041667 0.82753165]
----------------------------------------
Trial 4298
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
n_estimators=163, random_state=100,
subsample=0.75))])
cv score: [0.75833333 0.65416667 0.78333333 0.83916667 0.86471519]
----------------------------------------
Trial 4299
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73 0.77166667 0.78375 0.85708333 0.92246835]
----------------------------------------
Trial 4300
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=175,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77666667 0.72833333 0.79166667 0.84416667 0.88607595]
----------------------------------------
Trial 4301
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=193, random_state=100,
subsample=0.95))])
cv score: [0.59083333 0.55833333 0.6925 0.785 0.78955696]
----------------------------------------
Trial 4302
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
random_state=100))])
cv score: [0.6875 0.6425 0.73166667 0.80833333 0.82911392]
----------------------------------------
Trial 4303
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63 0.5825 0.75083333 0.68083333 0.73655063]
----------------------------------------
Trial 4304
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=28,
random_state=100))])
cv score: [0.62166667 0.6475 0.68666667 0.85208333 0.82990506]
----------------------------------------
Trial 4305
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='log2',
n_estimators=66, random_state=100,
subsample=0.85))])
cv score: [0.67 0.6575 0.72 0.79416667 0.84335443]
----------------------------------------
Trial 4306
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71416667 0.72208333 0.77416667 0.81041667 0.94026899]
----------------------------------------
Trial 4307
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=192,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67916667 0.66416667 0.71416667 0.77166667 0.80142405]
----------------------------------------
Trial 4308
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=164, random_state=100,
subsample=0.6))])
cv score: [0.64583333 0.5125 0.66416667 0.70166667 0.71835443]
----------------------------------------
Trial 4309
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=133,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72166667 0.69666667 0.78 0.79583333 0.90189873]
----------------------------------------
Trial 4310
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6425 0.69333333 0.78583333 0.76083333 0.85522152]
----------------------------------------
Trial 4311
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='sqrt',
n_estimators=163,
random_state=100))])
cv score: [0.68083333 0.58833333 0.70916667 0.77333333 0.76898734]
----------------------------------------
Trial 4312
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.6))])
cv score: [0.42 0.6825 0.565 0.65333333 0.5443038 ]
----------------------------------------
Trial 4313
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=172, random_state=100))])
cv score: [0.78916667 0.68416667 0.7625 0.84666667 0.87262658]
----------------------------------------
Trial 4314
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='log2',
n_estimators=46, random_state=100,
subsample=0.85))])
cv score: [0.69333333 0.66583333 0.73 0.80416667 0.81329114]
----------------------------------------
Trial 4315
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=41,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 4316
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68 0.6825 0.74416667 0.76083333 0.82753165]
----------------------------------------
Trial 4317
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=113,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.66166667 0.75833333 0.72 0.82436709]
----------------------------------------
Trial 4318
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=199, random_state=100,
subsample=0.85))])
cv score: [0.75416667 0.63 0.80583333 0.82583333 0.82911392]
----------------------------------------
Trial 4319
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66208333 0.7425 0.76583333 0.83458333 0.88053797]
----------------------------------------
Trial 4320
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
n_estimators=69, random_state=100,
subsample=0.85))])
cv score: [0.705 0.62666667 0.77083333 0.79666667 0.78718354]
----------------------------------------
Trial 4321
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76583333 0.72125 0.76 0.86541667 0.90031646]
----------------------------------------
Trial 4322
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=155,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77083333 0.7225 0.78166667 0.86333333 0.87737342]
----------------------------------------
Trial 4323
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71 0.725 0.77416667 0.83916667 0.91772152]
----------------------------------------
Trial 4324
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
n_estimators=135,
random_state=100))])
cv score: [0.75416667 0.62666667 0.73416667 0.86583333 0.76661392]
----------------------------------------
Trial 4325
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, n_estimators=19,
random_state=100, subsample=0.9))])
cv score: [0.72416667 0.63458333 0.76291667 0.83333333 0.84177215]
----------------------------------------
Trial 4326
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71 0.72083333 0.8025 0.8425 0.88844937]
----------------------------------------
Trial 4327
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=64, random_state=100,
subsample=0.65))])
cv score: [0.59583333 0.58916667 0.72166667 0.63333333 0.74841772]
----------------------------------------
Trial 4328
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=82,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69041667 0.74833333 0.76083333 0.84375 0.88528481]
----------------------------------------
Trial 4329
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=135, random_state=100,
subsample=0.75))])
cv score: [0.685 0.64083333 0.68333333 0.78333333 0.84256329]
----------------------------------------
Trial 4330
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60083333 0.62416667 0.74583333 0.68666667 0.71518987]
----------------------------------------
Trial 4331
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='sqrt',
n_estimators=46,
random_state=100))])
cv score: [0.59 0.61916667 0.6725 0.72416667 0.74208861]
----------------------------------------
Trial 4332
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=35,
random_state=100))])
cv score: [0.75583333 0.58791667 0.81083333 0.78708333 0.80379747]
----------------------------------------
Trial 4333
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=124, random_state=100,
subsample=0.6))])
cv score: [0.655 0.6275 0.675 0.78083333 0.81329114]
----------------------------------------
Trial 4334
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=152,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.66291667 0.78876582]
----------------------------------------
Trial 4335
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69416667 0.6425 0.73 0.76 0.79351266]
----------------------------------------
Trial 4336
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=137, random_state=100,
subsample=0.95))])
cv score: [0.5825 0.57833333 0.6025 0.66083333 0.67642405]
----------------------------------------
Trial 4337
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=119, random_state=100))])
cv score: [0.6925 0.63416667 0.7225 0.805 0.84256329]
----------------------------------------
Trial 4338
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='sqrt',
n_estimators=26, random_state=100,
subsample=0.75))])
cv score: [0.64 0.64833333 0.64583333 0.6825 0.69382911]
----------------------------------------
Trial 4339
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=12,
random_state=100))])
cv score: [0.62416667 0.60083333 0.69666667 0.645 0.75158228]
----------------------------------------
Trial 4340
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7325 0.69083333 0.79166667 0.83416667 0.88844937]
----------------------------------------
Trial 4341
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=54,
random_state=100))])
cv score: [0.52375 0.67541667 0.70916667 0.815 0.79113924]
----------------------------------------
Trial 4342
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63791667 0.71375 0.67625 0.79583333 0.78481013]
----------------------------------------
Trial 4343
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=155,
random_state=100))])
cv score: [0.65416667 0.67333333 0.69583333 0.81916667 0.84493671]
----------------------------------------
Trial 4344
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=33,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53291667 0.6775 0.65625 0.83041667 0.75356013]
----------------------------------------
Trial 4345
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=30,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.735 0.72125 0.79458333 0.845 0.90031646]
----------------------------------------
Trial 4346
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=30, random_state=100,
subsample=0.9))])
cv score: [0.55583333 0.675 0.56416667 0.71416667 0.66218354]
----------------------------------------
Trial 4347
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='sqrt',
n_estimators=178, random_state=100,
subsample=0.85))])
cv score: [0.63166667 0.64333333 0.655 0.72 0.65743671]
----------------------------------------
Trial 4348
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=12,
random_state=100))])
cv score: [0.7425 0.5575 0.67 0.77083333 0.80696203]
----------------------------------------
Trial 4349
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=106,
random_state=100))])
cv score: [0.69833333 0.63791667 0.70416667 0.805 0.82515823]
----------------------------------------
Trial 4350
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5375 0.6275 0.74166667 0.705 0.70094937]
----------------------------------------
Trial 4351
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.75))])
cv score: [0.64833333 0.63916667 0.7225 0.7975 0.83306962]
----------------------------------------
Trial 4352
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
n_estimators=112, random_state=100,
subsample=0.65))])
cv score: [0.73 0.67625 0.75083333 0.85875 0.87579114]
----------------------------------------
Trial 4353
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=197, random_state=100,
subsample=0.95))])
cv score: [0.725 0.57916667 0.70416667 0.79083333 0.78085443]
----------------------------------------
Trial 4354
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=173, random_state=100))])
cv score: [0.62791667 0.69 0.66583333 0.82583333 0.82950949]
----------------------------------------
Trial 4355
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=109,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67 0.66416667 0.7825 0.79416667 0.87658228]
----------------------------------------
Trial 4356
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.685 0.6675 0.7875 0.76666667 0.86075949]
----------------------------------------
Trial 4357
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.75))])
cv score: [0.75 0.57333333 0.71916667 0.7825 0.83781646]
----------------------------------------
Trial 4358
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=40, random_state=100,
subsample=0.7))])
cv score: [0.64833333 0.63333333 0.7225 0.66 0.54351266]
----------------------------------------
Trial 4359
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.52166667 0.73708333 0.755 0.80333333 0.8477057 ]
----------------------------------------
Trial 4360
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='sqrt',
n_estimators=194,
random_state=100))])
cv score: [0.64666667 0.59416667 0.6975 0.76 0.7460443 ]
----------------------------------------
Trial 4361
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=155,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69833333 0.66583333 0.79 0.80083333 0.87816456]
----------------------------------------
Trial 4362
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
n_estimators=29,
random_state=100))])
cv score: [0.76125 0.52458333 0.75875 0.76541667 0.67128165]
----------------------------------------
Trial 4363
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5575 0.63666667 0.7525 0.71916667 0.87579114]
----------------------------------------
Trial 4364
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=32, random_state=100))])
cv score: [0.61666667 0.73541667 0.70875 0.83416667 0.88488924]
----------------------------------------
Trial 4365
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=123,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64583333 0.63583333 0.76083333 0.7375 0.80537975]
----------------------------------------
Trial 4366
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=25,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71125 0.74 0.795 0.84916667 0.83939873]
----------------------------------------
Trial 4367
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=137, random_state=100))])
cv score: [0.69416667 0.62583333 0.72 0.8025 0.85047468]
----------------------------------------
Trial 4368
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=62,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.59916667 0.6375 0.71833333 0.65083333 0.8085443 ]
----------------------------------------
Trial 4369
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=176,
random_state=100,
subsample=0.85))])
cv score: [0.68916667 0.61583333 0.76833333 0.83 0.79905063]
----------------------------------------
Trial 4370
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=54,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.75791667 0.8075 0.86333333 0.84414557]
----------------------------------------
Trial 4371
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6975 0.6925 0.76166667 0.80166667 0.86708861]
----------------------------------------
Trial 4372
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=187,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77375 0.72333333 0.76833333 0.86 0.8528481 ]
----------------------------------------
Trial 4373
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=62,
random_state=100))])
cv score: [0.62958333 0.50083333 0.58583333 0.82333333 0.71202532]
----------------------------------------
Trial 4374
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=27,
random_state=100))])
cv score: [0.71333333 0.64125 0.73916667 0.78833333 0.85047468]
----------------------------------------
Trial 4375
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=63,
random_state=100))])
cv score: [0.60875 0.59208333 0.73291667 0.74583333 0.80775316]
----------------------------------------
Trial 4376
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=78,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56875 0.72125 0.69416667 0.82541667 0.82357595]
----------------------------------------
Trial 4377
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
n_estimators=159, random_state=100,
subsample=0.7))])
cv score: [0.73833333 0.66916667 0.8 0.83416667 0.83544304]
----------------------------------------
Trial 4378
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=114,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.7025 0.78083333 0.84916667 0.91297468]
----------------------------------------
Trial 4379
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=68, random_state=100))])
cv score: [0.72333333 0.67125 0.77666667 0.82833333 0.84256329]
----------------------------------------
Trial 4380
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=102,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.69 0.805 0.83583333 0.86787975]
----------------------------------------
Trial 4381
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=107,
random_state=100))])
cv score: [0.55666667 0.605 0.68666667 0.71666667 0.56408228]
----------------------------------------
Trial 4382
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=53, random_state=100))])
cv score: [0.71875 0.67541667 0.78083333 0.83916667 0.84177215]
----------------------------------------
Trial 4383
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=131, random_state=100))])
cv score: [0.69166667 0.63916667 0.73916667 0.81333333 0.82911392]
----------------------------------------
Trial 4384
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=43, random_state=100,
subsample=0.9))])
cv score: [0.74916667 0.63666667 0.8125 0.83416667 0.72863924]
----------------------------------------
Trial 4385
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
n_estimators=106, random_state=100,
subsample=0.8))])
cv score: [0.71 0.68666667 0.765 0.82 0.79984177]
----------------------------------------
Trial 4386
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=111,
random_state=100))])
cv score: [0.61166667 0.67666667 0.7 0.81083333 0.82753165]
----------------------------------------
Trial 4387
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=73, random_state=100))])
cv score: [0.69583333 0.6675 0.7275 0.7925 0.86867089]
----------------------------------------
Trial 4388
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=133, random_state=100,
subsample=0.8))])
cv score: [0.685 0.635 0.72166667 0.8025 0.79746835]
----------------------------------------
Trial 4389
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='sqrt',
n_estimators=188, random_state=100,
subsample=0.85))])
cv score: [0.5825 0.56083333 0.64666667 0.75833333 0.68670886]
----------------------------------------
Trial 4390
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=62,
random_state=100))])
cv score: [0.53208333 0.66041667 0.72 0.83333333 0.78481013]
----------------------------------------
Trial 4391
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=199, random_state=100,
subsample=0.85))])
cv score: [0.65666667 0.5725 0.66083333 0.71 0.77768987]
----------------------------------------
Trial 4392
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=120, random_state=100,
subsample=0.95))])
cv score: [0.7475 0.62958333 0.82166667 0.8375 0.73259494]
----------------------------------------
Trial 4393
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76083333 0.73 0.78333333 0.83166667 0.90348101]
----------------------------------------
Trial 4394
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=31,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.74 0.78791667 0.83833333 0.87579114]
----------------------------------------
Trial 4395
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=43, random_state=100))])
cv score: [0.67416667 0.68958333 0.68416667 0.83 0.86155063]
----------------------------------------
Trial 4396
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=159,
random_state=100))])
cv score: [0.67916667 0.62083333 0.70833333 0.80166667 0.82753165]
----------------------------------------
Trial 4397
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63416667 0.6525 0.7775 0.73083333 0.80142405]
----------------------------------------
Trial 4398
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66416667 0.64666667 0.8025 0.75916667 0.82911392]
----------------------------------------
Trial 4399
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=74, random_state=100))])
cv score: [0.61 0.68333333 0.71083333 0.7825 0.82436709]
----------------------------------------
Trial 4400
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=144,
random_state=100))])
cv score: [0.65083333 0.66666667 0.69666667 0.82 0.84810127]
----------------------------------------
Trial 4401
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=55,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77541667 0.75583333 0.78833333 0.8475 0.87183544]
----------------------------------------
Trial 4402
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=51, random_state=100))])
cv score: [0.7175 0.6725 0.775 0.84083333 0.84177215]
----------------------------------------
Trial 4403
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=14, random_state=100))])
cv score: [0.76583333 0.67625 0.68833333 0.86208333 0.85522152]
----------------------------------------
Trial 4404
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=103,
random_state=100))])
cv score: [0.64833333 0.665 0.69583333 0.8075 0.84335443]
----------------------------------------
Trial 4405
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65583333 0.55833333 0.79416667 0.7525 0.79272152]
----------------------------------------
Trial 4406
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=136, random_state=100,
subsample=0.8))])
cv score: [0.62583333 0.59833333 0.66333333 0.77083333 0.70411392]
----------------------------------------
Trial 4407
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=102, random_state=100))])
cv score: [0.69916667 0.63416667 0.74166667 0.78333333 0.81170886]
----------------------------------------
Trial 4408
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=23,
random_state=100))])
cv score: [0.75916667 0.64416667 0.69833333 0.7625 0.78401899]
----------------------------------------
Trial 4409
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=111,
random_state=100))])
cv score: [0.67916667 0.61416667 0.72083333 0.80833333 0.82199367]
----------------------------------------
Trial 4410
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=77,
random_state=100))])
cv score: [0.69833333 0.58666667 0.68333333 0.78875 0.79746835]
----------------------------------------
Trial 4411
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=70,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61666667 0.5925 0.7775 0.745 0.68670886]
----------------------------------------
Trial 4412
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=119,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66416667 0.685 0.76833333 0.78333333 0.87816456]
----------------------------------------
Trial 4413
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=189,
random_state=100))])
cv score: [0.69416667 0.51791667 0.7975 0.66958333 0.62816456]
----------------------------------------
Trial 4414
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=14,
random_state=100))])
cv score: [0.76291667 0.49375 0.78791667 0.70166667 0.79786392]
----------------------------------------
Trial 4415
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=97, random_state=100))])
cv score: [0.70583333 0.625 0.72166667 0.79166667 0.83227848]
----------------------------------------
Trial 4416
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68333333 0.67416667 0.79583333 0.79083333 0.85917722]
----------------------------------------
Trial 4417
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=76,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72416667 0.74208333 0.77041667 0.84166667 0.87420886]
----------------------------------------
Trial 4418
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77916667 0.7 0.795 0.8275 0.88528481]
----------------------------------------
Trial 4419
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=187,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.605 0.61833333 0.7725 0.72833333 0.81882911]
----------------------------------------
Trial 4420
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=88, random_state=100))])
cv score: [0.6925 0.62041667 0.7225 0.82541667 0.82792722]
----------------------------------------
Trial 4421
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=91, random_state=100,
subsample=0.95))])
cv score: [0.5475 0.60416667 0.66 0.72333333 0.79825949]
----------------------------------------
Trial 4422
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
n_estimators=134, random_state=100,
subsample=0.85))])
cv score: [0.71583333 0.65916667 0.80166667 0.83 0.80458861]
----------------------------------------
Trial 4423
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=41, random_state=100,
subsample=0.9))])
cv score: [0.71166667 0.63041667 0.78625 0.82666667 0.76740506]
----------------------------------------
Trial 4424
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=37,
random_state=100))])
cv score: [0.72333333 0.63666667 0.6725 0.775 0.79588608]
----------------------------------------
Trial 4425
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=107,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.675 0.67 0.77083333 0.78916667 0.86629747]
----------------------------------------
Trial 4426
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='sqrt',
n_estimators=56, random_state=100,
subsample=0.7))])
cv score: [0.67333333 0.54083333 0.69833333 0.79166667 0.81329114]
----------------------------------------
Trial 4427
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=41, random_state=100))])
cv score: [0.75333333 0.6725 0.7775 0.84083333 0.83939873]
----------------------------------------
Trial 4428
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=152, random_state=100))])
cv score: [0.69083333 0.63583333 0.74416667 0.79166667 0.82278481]
----------------------------------------
Trial 4429
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=195,
random_state=100))])
cv score: [0.68708333 0.51375 0.82458333 0.66708333 0.66416139]
----------------------------------------
Trial 4430
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.65))])
cv score: [0.69333333 0.61166667 0.7275 0.78166667 0.79905063]
----------------------------------------
Trial 4431
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=77, random_state=100,
subsample=0.7))])
cv score: [0.66333333 0.58666667 0.705 0.80916667 0.75870253]
----------------------------------------
Trial 4432
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=195, random_state=100))])
cv score: [0.59916667 0.68333333 0.7075 0.81166667 0.83227848]
----------------------------------------
Trial 4433
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=43, random_state=100))])
cv score: [0.64083333 0.67166667 0.70333333 0.78416667 0.82753165]
----------------------------------------
Trial 4434
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=85,
random_state=100))])
cv score: [0.51625 0.66541667 0.71666667 0.81416667 0.76107595]
----------------------------------------
Trial 4435
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71916667 0.72833333 0.74291667 0.86583333 0.83544304]
----------------------------------------
Trial 4436
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
n_estimators=172, random_state=100,
subsample=0.65))])
cv score: [0.74083333 0.67333333 0.7975 0.83416667 0.84256329]
----------------------------------------
Trial 4437
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=157, random_state=100,
subsample=0.9))])
cv score: [0.76 0.62083333 0.83416667 0.8375 0.78639241]
----------------------------------------
Trial 4438
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
n_estimators=28, random_state=100,
subsample=0.75))])
cv score: [0.715 0.66083333 0.81916667 0.80833333 0.82278481]
----------------------------------------
Trial 4439
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=189, random_state=100,
subsample=0.65))])
cv score: [0.705 0.67333333 0.75 0.82666667 0.83148734]
----------------------------------------
Trial 4440
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=147,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71916667 0.73083333 0.7575 0.86666667 0.84177215]
----------------------------------------
Trial 4441
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73458333 0.72625 0.7725 0.86625 0.8619462 ]
----------------------------------------
Trial 4442
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=109,
random_state=100))])
cv score: [0.60916667 0.67583333 0.70083333 0.81 0.82832278]
----------------------------------------
Trial 4443
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=73,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.535 0.58166667 0.7375 0.76333333 0.77768987]
----------------------------------------
Trial 4444
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=144, random_state=100,
subsample=0.8))])
cv score: [0.655 0.58083333 0.68583333 0.7325 0.7096519 ]
----------------------------------------
Trial 4445
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
n_estimators=123, random_state=100,
subsample=0.75))])
cv score: [0.7775 0.6775 0.7775 0.8525 0.87658228]
----------------------------------------
Trial 4446
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=28,
random_state=100))])
cv score: [0.63458333 0.57666667 0.67125 0.78333333 0.83623418]
----------------------------------------
Trial 4447
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=168,
random_state=100))])
cv score: [0.70666667 0.60333333 0.67333333 0.805 0.83860759]
----------------------------------------
Trial 4448
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.51333333 0.565 0.57291667 0.76791667 0.71123418]
----------------------------------------
Trial 4449
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.6725 0.80833333 0.83333333 0.87579114]
----------------------------------------
Trial 4450
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.73166667 0.76916667 0.86666667 0.86787975]
----------------------------------------
Trial 4451
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=91, random_state=100))])
cv score: [0.66916667 0.665 0.69333333 0.8225 0.86867089]
----------------------------------------
Trial 4452
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=81, random_state=100))])
cv score: [0.67083333 0.6075 0.72541667 0.81875 0.81685127]
----------------------------------------
Trial 4453
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=173,
random_state=100))])
cv score: [0.56541667 0.66458333 0.7025 0.82125 0.81091772]
----------------------------------------
Trial 4454
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=176, random_state=100))])
cv score: [0.75 0.67583333 0.78 0.83416667 0.84651899]
----------------------------------------
Trial 4455
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=177, random_state=100))])
cv score: [0.705 0.62916667 0.72833333 0.80083333 0.82515823]
----------------------------------------
Trial 4456
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=179,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68833333 0.66833333 0.79166667 0.78416667 0.83386076]
----------------------------------------
Trial 4457
----------------------------------------
Parameters {'gb__n_estimators': 88, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=88,
random_state=100))])
cv score: [0.67916667 0.6425 0.7125 0.78125 0.83702532]
----------------------------------------
Trial 4458
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=74,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69833333 0.73583333 0.7425 0.85916667 0.83386076]
----------------------------------------
Trial 4459
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=14,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73666667 0.685 0.71916667 0.69916667 0.51424051]
----------------------------------------
Trial 4460
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=138, random_state=100,
subsample=0.75))])
cv score: [0.69166667 0.64 0.68083333 0.7775 0.84335443]
----------------------------------------
Trial 4461
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=58,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.72333333 0.75666667 0.86416667 0.83702532]
----------------------------------------
Trial 4462
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=19, random_state=100,
subsample=0.65))])
cv score: [0.64 0.6675 0.62916667 0.79666667 0.85126582]
----------------------------------------
Trial 4463
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='sqrt',
n_estimators=47, random_state=100,
subsample=0.85))])
cv score: [0.59916667 0.655 0.74 0.7575 0.68275316]
----------------------------------------
Trial 4464
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71083333 0.69833333 0.79916667 0.80416667 0.88212025]
----------------------------------------
Trial 4465
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=34,
random_state=100))])
cv score: [0.72 0.58333333 0.725 0.74833333 0.77848101]
----------------------------------------
Trial 4466
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=155,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75833333 0.73166667 0.75958333 0.86541667 0.88212025]
----------------------------------------
Trial 4467
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=80, random_state=100))])
cv score: [0.68333333 0.64583333 0.73541667 0.81375 0.82041139]
----------------------------------------
Trial 4468
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=20,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 4469
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=132,
random_state=100))])
cv score: [0.69 0.63333333 0.70166667 0.8075 0.82594937]
----------------------------------------
Trial 4470
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.735 0.78916667 0.83125 0.86392405]
----------------------------------------
Trial 4471
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=23, random_state=100))])
cv score: [0.60583333 0.65916667 0.68625 0.79666667 0.84256329]
----------------------------------------
Trial 4472
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=67, random_state=100,
subsample=0.8))])
cv score: [0.57916667 0.62916667 0.66666667 0.70083333 0.78401899]
----------------------------------------
Trial 4473
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=189,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74 0.7125 0.76958333 0.85416667 0.87262658]
----------------------------------------
Trial 4474
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=132,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63416667 0.61583333 0.68833333 0.77166667 0.75870253]
----------------------------------------
Trial 4475
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=54, random_state=100))])
cv score: [0.75083333 0.6925 0.77 0.84666667 0.8931962 ]
----------------------------------------
Trial 4476
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=93,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.765 0.715 0.80333333 0.835 0.8931962 ]
----------------------------------------
Trial 4477
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='sqrt',
n_estimators=125,
random_state=100))])
cv score: [0.70166667 0.6 0.6975 0.785 0.81170886]
----------------------------------------
Trial 4478
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
n_estimators=66, random_state=100,
subsample=0.7))])
cv score: [0.63333333 0.53333333 0.78583333 0.745 0.81012658]
----------------------------------------
Trial 4479
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=132, random_state=100,
subsample=0.75))])
cv score: [0.6225 0.585 0.65166667 0.71333333 0.65585443]
----------------------------------------
Trial 4480
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='sqrt',
n_estimators=64, random_state=100,
subsample=0.65))])
cv score: [0.58583333 0.50583333 0.59416667 0.665 0.83623418]
----------------------------------------
Trial 4481
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=129, random_state=100,
subsample=0.65))])
cv score: [0.6625 0.6275 0.68083333 0.80166667 0.82041139]
----------------------------------------
Trial 4482
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=156, random_state=100))])
cv score: [0.6775 0.6325 0.745 0.81375 0.82238924]
----------------------------------------
Trial 4483
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=170,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71833333 0.72333333 0.7675 0.8475 0.91376582]
----------------------------------------
Trial 4484
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.85))])
cv score: [0.66583333 0.59416667 0.7375 0.77583333 0.73655063]
----------------------------------------
Trial 4485
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=135, random_state=100))])
cv score: [0.68083333 0.62666667 0.73833333 0.80916667 0.83227848]
----------------------------------------
Trial 4486
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73958333 0.73875 0.77875 0.86333333 0.91455696]
----------------------------------------
Trial 4487
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=41, random_state=100))])
cv score: [0.75166667 0.6825 0.76833333 0.84833333 0.88924051]
----------------------------------------
Trial 4488
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=195,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56583333 0.64083333 0.73666667 0.7475 0.84335443]
----------------------------------------
Trial 4489
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
n_estimators=194,
random_state=100))])
cv score: [0.82 0.60833333 0.74833333 0.87041667 0.81685127]
----------------------------------------
Trial 4490
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=57, random_state=100,
subsample=0.9))])
cv score: [0.68 0.60583333 0.72583333 0.79166667 0.85759494]
----------------------------------------
Trial 4491
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=47,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77 0.7525 0.81083333 0.85833333 0.88528481]
----------------------------------------
Trial 4492
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=175, random_state=100))])
cv score: [0.72791667 0.67666667 0.77875 0.83833333 0.83781646]
----------------------------------------
Trial 4493
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=148,
random_state=100))])
cv score: [0.6925 0.50125 0.72416667 0.64958333 0.63251582]
----------------------------------------
Trial 4494
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=175,
random_state=100))])
cv score: [0.69416667 0.51791667 0.79833333 0.66916667 0.6289557 ]
----------------------------------------
Trial 4495
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65083333 0.67 0.77666667 0.80083333 0.86629747]
----------------------------------------
Trial 4496
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=120, random_state=100,
subsample=0.7))])
cv score: [0.68833333 0.65 0.75416667 0.82 0.79905063]
----------------------------------------
Trial 4497
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='log2',
n_estimators=72,
random_state=100))])
cv score: [0.695 0.66583333 0.70666667 0.77416667 0.75632911]
----------------------------------------
Trial 4498
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=76, random_state=100,
subsample=0.7))])
cv score: [0.74375 0.69583333 0.71666667 0.86166667 0.86906646]
----------------------------------------
Trial 4499
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=197, random_state=100,
subsample=0.65))])
cv score: [0.6775 0.63166667 0.7325 0.80166667 0.81566456]
----------------------------------------
Trial 4500
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=166,
random_state=100))])
cv score: [0.6925 0.50125 0.72375 0.64875 0.63291139]
----------------------------------------
Trial 4501
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78833333 0.625 0.76166667 0.78916667 0.84018987]
----------------------------------------
Trial 4502
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=73, random_state=100,
subsample=0.6))])
cv score: [0.76 0.58416667 0.73166667 0.8075 0.80696203]
----------------------------------------
Trial 4503
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=93, random_state=100))])
cv score: [0.69166667 0.66916667 0.73083333 0.7975 0.86075949]
----------------------------------------
Trial 4504
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=171,
random_state=100))])
cv score: [0.56458333 0.68041667 0.70291667 0.82 0.76582278]
----------------------------------------
Trial 4505
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75166667 0.71 0.8 0.85583333 0.90506329]
----------------------------------------
Trial 4506
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=150,
random_state=100))])
cv score: [0.69416667 0.51791667 0.79916667 0.67041667 0.62856013]
----------------------------------------
Trial 4507
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=104,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66 0.67333333 0.75 0.72083333 0.75553797]
----------------------------------------
Trial 4508
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61416667 0.65416667 0.78083333 0.77833333 0.84572785]
----------------------------------------
Trial 4509
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75333333 0.69 0.76916667 0.81833333 0.91851266]
----------------------------------------
Trial 4510
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=120,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68833333 0.665 0.76666667 0.79166667 0.87025316]
----------------------------------------
Trial 4511
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, n_estimators=79,
random_state=100))])
cv score: [0.73666667 0.6475 0.7675 0.79 0.79113924]
----------------------------------------
Trial 4512
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=11, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=172,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7775 0.72291667 0.78583333 0.84083333 0.86708861]
----------------------------------------
Trial 4513
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=50, random_state=100,
subsample=0.7))])
cv score: [0.66333333 0.64166667 0.71166667 0.78 0.83227848]
----------------------------------------
Trial 4514
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=66, random_state=100,
subsample=0.6))])
cv score: [0.635 0.68833333 0.7 0.80083333 0.79984177]
----------------------------------------
Trial 4515
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=47,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.72541667 0.78083333 0.84416667 0.91851266]
----------------------------------------
Trial 4516
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7175 0.6925 0.7925 0.82583333 0.89477848]
----------------------------------------
Trial 4517
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=133,
random_state=100))])
cv score: [0.66791667 0.54291667 0.82416667 0.78041667 0.78322785]
----------------------------------------
Trial 4518
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=197,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75583333 0.73291667 0.77666667 0.8575 0.86075949]
----------------------------------------
Trial 4519
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=135,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.8375 0.82199367]
----------------------------------------
Trial 4520
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=91, random_state=100,
subsample=0.65))])
cv score: [0.62916667 0.52916667 0.59166667 0.635 0.69541139]
----------------------------------------
Trial 4521
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
n_estimators=44, random_state=100,
subsample=0.75))])
cv score: [0.72083333 0.64333333 0.78583333 0.78916667 0.80221519]
----------------------------------------
Trial 4522
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=59,
random_state=100))])
cv score: [0.69083333 0.50125 0.72416667 0.65041667 0.63212025]
----------------------------------------
Trial 4523
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=98,
random_state=100))])
cv score: [0.76916667 0.49375 0.78791667 0.69833333 0.79232595]
----------------------------------------
Trial 4524
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=108,
random_state=100))])
cv score: [0.6875 0.51375 0.82458333 0.66333333 0.66574367]
----------------------------------------
Trial 4525
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=131,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75083333 0.74041667 0.77916667 0.84583333 0.8710443 ]
----------------------------------------
Trial 4526
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64583333 0.64583333 0.75166667 0.7475 0.79905063]
----------------------------------------
Trial 4527
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=109, random_state=100))])
cv score: [0.69083333 0.62916667 0.72166667 0.80916667 0.83306962]
----------------------------------------
Trial 4528
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='sqrt',
n_estimators=15,
random_state=100))])
cv score: [0.54833333 0.61166667 0.74 0.87291667 0.77650316]
----------------------------------------
Trial 4529
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=166,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75416667 0.70666667 0.80583333 0.82583333 0.88686709]
----------------------------------------
Trial 4530
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=97,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69333333 0.67416667 0.76916667 0.81 0.88844937]
----------------------------------------
Trial 4531
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=179,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.765 0.715 0.78166667 0.84833333 0.88370253]
----------------------------------------
Trial 4532
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=84,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73916667 0.73083333 0.7925 0.85083333 0.91772152]
----------------------------------------
Trial 4533
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=156, random_state=100))])
cv score: [0.72333333 0.67666667 0.77666667 0.83666667 0.82990506]
----------------------------------------
Trial 4534
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.55666667 0.72166667 0.76083333 0.74762658]
----------------------------------------
Trial 4535
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='sqrt',
n_estimators=85, random_state=100,
subsample=0.75))])
cv score: [0.66916667 0.67 0.69333333 0.8325 0.82041139]
----------------------------------------
Trial 4536
----------------------------------------
Parameters {'xgb__n_estimators': 99, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=99,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7725 0.73333333 0.79166667 0.855 0.87816456]
----------------------------------------
Trial 4537
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72 0.73333333 0.74833333 0.86916667 0.84572785]
----------------------------------------
Trial 4538
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='log2',
n_estimators=43, random_state=100,
subsample=0.75))])
cv score: [0.73416667 0.66916667 0.74916667 0.7825 0.84572785]
----------------------------------------
Trial 4539
----------------------------------------
Parameters {'xgb__n_estimators': 26, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=26,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.765 0.74125 0.80541667 0.82083333 0.90031646]
----------------------------------------
Trial 4540
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=68, random_state=100))])
cv score: [0.72333333 0.67125 0.77666667 0.82833333 0.84256329]
----------------------------------------
Trial 4541
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73 0.75291667 0.79083333 0.78583333 0.92088608]
----------------------------------------
Trial 4542
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=45, random_state=100,
subsample=0.65))])
cv score: [0.47583333 0.58333333 0.65 0.69666667 0.67246835]
----------------------------------------
Trial 4543
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=23,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66541667 0.74916667 0.76666667 0.83208333 0.87658228]
----------------------------------------
Trial 4544
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=126,
random_state=100))])
cv score: [0.65708333 0.59208333 0.72375 0.7875 0.77175633]
----------------------------------------
Trial 4545
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=158,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.62333333 0.74333333 0.745 0.79746835]
----------------------------------------
Trial 4546
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=72, random_state=100,
subsample=0.8))])
cv score: [0.68583333 0.665 0.62333333 0.70166667 0.71202532]
----------------------------------------
Trial 4547
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=164,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.6925 0.795 0.82666667 0.89952532]
----------------------------------------
Trial 4548
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=136, random_state=100))])
cv score: [0.66416667 0.66666667 0.74416667 0.79416667 0.84098101]
----------------------------------------
Trial 4549
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=61, random_state=100))])
cv score: [0.6625 0.6075 0.71083333 0.79625 0.82120253]
----------------------------------------
Trial 4550
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=15,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.52083333 0.72625 0.73583333 0.81125 0.85799051]
----------------------------------------
Trial 4551
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=102, random_state=100))])
cv score: [0.69916667 0.63416667 0.74166667 0.78333333 0.81170886]
----------------------------------------
Trial 4552
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=98,
random_state=100))])
cv score: [0.68083333 0.58833333 0.68666667 0.81416667 0.83148734]
----------------------------------------
Trial 4553
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=129,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.755 0.73166667 0.77541667 0.86166667 0.86234177]
----------------------------------------
Trial 4554
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=90, random_state=100,
subsample=0.65))])
cv score: [0.60666667 0.68666667 0.70583333 0.83333333 0.81566456]
----------------------------------------
Trial 4555
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
n_estimators=195, random_state=100,
subsample=0.95))])
cv score: [0.81083333 0.67333333 0.75041667 0.87083333 0.84454114]
----------------------------------------
Trial 4556
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=106,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79666667 0.73416667 0.79125 0.84833333 0.86313291]
----------------------------------------
Trial 4557
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64416667 0.615 0.80833333 0.79166667 0.76107595]
----------------------------------------
Trial 4558
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=148,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78 0.74916667 0.78916667 0.8625 0.86075949]
----------------------------------------
Trial 4559
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=72,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78666667 0.73875 0.7975 0.83416667 0.85443038]
----------------------------------------
Trial 4560
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='sqrt',
n_estimators=78, random_state=100,
subsample=0.8))])
cv score: [0.70083333 0.615 0.72083333 0.80583333 0.80537975]
----------------------------------------
Trial 4561
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=118,
random_state=100))])
cv score: [0.65708333 0.60041667 0.72541667 0.78166667 0.76147152]
----------------------------------------
Trial 4562
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=175,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.65833333 0.7925 0.77583333 0.85126582]
----------------------------------------
Trial 4563
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72166667 0.7275 0.76416667 0.84416667 0.86155063]
----------------------------------------
Trial 4564
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=112,
random_state=100))])
cv score: [0.56291667 0.65875 0.6925 0.83375 0.7994462 ]
----------------------------------------
Trial 4565
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='sqrt',
n_estimators=113, random_state=100,
subsample=0.65))])
cv score: [0.70416667 0.6575 0.73 0.82583333 0.81566456]
----------------------------------------
Trial 4566
----------------------------------------
Parameters {'gb__n_estimators': 55, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=55,
random_state=100))])
cv score: [0.64416667 0.59333333 0.725 0.76166667 0.75712025]
----------------------------------------
Trial 4567
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=37, random_state=100,
subsample=0.8))])
cv score: [0.59083333 0.59333333 0.615 0.66833333 0.75870253]
----------------------------------------
Trial 4568
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=96, random_state=100,
subsample=0.6))])
cv score: [0.655 0.64833333 0.71583333 0.78166667 0.79746835]
----------------------------------------
Trial 4569
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=141,
random_state=100,
subsample=0.75))])
cv score: [0.67916667 0.6425 0.77333333 0.81083333 0.8164557 ]
----------------------------------------
Trial 4570
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=130,
random_state=100))])
cv score: [0.62833333 0.6775 0.7025 0.81583333 0.82832278]
----------------------------------------
Trial 4571
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=69,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.535 0.71291667 0.70541667 0.8225 0.75712025]
----------------------------------------
Trial 4572
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=47, random_state=100))])
cv score: [0.675 0.62083333 0.73916667 0.80083333 0.85205696]
----------------------------------------
Trial 4573
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=110,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.645 0.6575 0.78333333 0.7175 0.82199367]
----------------------------------------
Trial 4574
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=196,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73666667 0.68666667 0.7875 0.835 0.89003165]
----------------------------------------
Trial 4575
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73375 0.75958333 0.76166667 0.85791667 0.84177215]
----------------------------------------
Trial 4576
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=50,
random_state=100))])
cv score: [0.52291667 0.66458333 0.69916667 0.82916667 0.78560127]
----------------------------------------
Trial 4577
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=41, random_state=100,
subsample=0.8))])
cv score: [0.60416667 0.71166667 0.7325 0.6325 0.77610759]
----------------------------------------
Trial 4578
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=126, random_state=100))])
cv score: [0.655 0.65416667 0.71 0.825 0.85443038]
----------------------------------------
Trial 4579
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=53,
random_state=100))])
cv score: [0.57208333 0.65791667 0.70791667 0.82208333 0.82041139]
----------------------------------------
Trial 4580
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.58166667 0.51583333 0.7525 0.715 0.76344937]
----------------------------------------
Trial 4581
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=150, random_state=100))])
cv score: [0.75666667 0.6775 0.7825 0.835 0.85126582]
----------------------------------------
Trial 4582
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=147, random_state=100))])
cv score: [0.735 0.68083333 0.7775 0.8375 0.83544304]
----------------------------------------
Trial 4583
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=112,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61166667 0.64916667 0.77833333 0.8125 0.81487342]
----------------------------------------
Trial 4584
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=138, random_state=100))])
cv score: [0.73666667 0.67958333 0.77958333 0.83791667 0.8346519 ]
----------------------------------------
Trial 4585
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=53,
random_state=100))])
cv score: [0.64333333 0.61583333 0.67833333 0.76583333 0.82278481]
----------------------------------------
Trial 4586
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=66,
random_state=100))])
cv score: [0.52625 0.65791667 0.72583333 0.82583333 0.77373418]
----------------------------------------
Trial 4587
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.75))])
cv score: [0.68666667 0.63666667 0.72833333 0.79833333 0.80537975]
----------------------------------------
Trial 4588
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65416667 0.6775 0.76583333 0.73791667 0.77689873]
----------------------------------------
Trial 4589
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=185, random_state=100))])
cv score: [0.695 0.64666667 0.70833333 0.80083333 0.83781646]
----------------------------------------
Trial 4590
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=42, random_state=100,
subsample=0.65))])
cv score: [0.645 0.62166667 0.71583333 0.82666667 0.8306962 ]
----------------------------------------
Trial 4591
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=84,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.6675 0.775 0.75666667 0.80458861]
----------------------------------------
Trial 4592
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='sqrt',
n_estimators=34, random_state=100,
subsample=0.85))])
cv score: [0.56583333 0.62416667 0.56666667 0.61583333 0.78164557]
----------------------------------------
Trial 4593
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=19,
random_state=100))])
cv score: [0.76625 0.49375 0.78958333 0.70208333 0.79786392]
----------------------------------------
Trial 4594
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=79,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7375 0.74208333 0.77875 0.83583333 0.84731013]
----------------------------------------
Trial 4595
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='sqrt',
n_estimators=137, random_state=100,
subsample=0.75))])
cv score: [0.67083333 0.62583333 0.64916667 0.73 0.72943038]
----------------------------------------
Trial 4596
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=125, random_state=100,
subsample=0.6))])
cv score: [0.71 0.64583333 0.6425 0.74833333 0.79193038]
----------------------------------------
Trial 4597
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
n_estimators=39, random_state=100,
subsample=0.85))])
cv score: [0.6925 0.64083333 0.82708333 0.8325 0.82120253]
----------------------------------------
Trial 4598
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=134, random_state=100,
subsample=0.8))])
cv score: [0.42333333 0.65416667 0.70333333 0.5125 0.47626582]
----------------------------------------
Trial 4599
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=66, random_state=100))])
cv score: [0.65208333 0.60458333 0.71583333 0.81041667 0.81843354]
----------------------------------------
Trial 4600
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=122,
random_state=100))])
cv score: [0.56375 0.65958333 0.68833333 0.83458333 0.7994462 ]
----------------------------------------
Trial 4601
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='log2',
n_estimators=41,
random_state=100))])
cv score: [0.72583333 0.56916667 0.65 0.73583333 0.75632911]
----------------------------------------
Trial 4602
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=134,
random_state=100))])
cv score: [0.65333333 0.66583333 0.70166667 0.82 0.84018987]
----------------------------------------
Trial 4603
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=51, random_state=100,
subsample=0.75))])
cv score: [0.59416667 0.5725 0.6075 0.6325 0.69778481]
----------------------------------------
Trial 4604
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=137, random_state=100,
subsample=0.65))])
cv score: [0.70333333 0.62083333 0.73833333 0.795 0.82674051]
----------------------------------------
Trial 4605
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=111, random_state=100,
subsample=0.7))])
cv score: [0.58833333 0.60666667 0.67583333 0.78916667 0.78797468]
----------------------------------------
Trial 4606
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='sqrt',
n_estimators=92, random_state=100,
subsample=0.85))])
cv score: [0.69333333 0.55416667 0.63 0.71166667 0.68037975]
----------------------------------------
Trial 4607
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=159, random_state=100,
subsample=0.85))])
cv score: [0.63166667 0.665 0.715 0.82083333 0.81803797]
----------------------------------------
Trial 4608
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=195, random_state=100))])
cv score: [0.69166667 0.64 0.72166667 0.80583333 0.84335443]
----------------------------------------
Trial 4609
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=152,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79583333 0.73916667 0.7975 0.83416667 0.86946203]
----------------------------------------
Trial 4610
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=86, random_state=100,
subsample=0.95))])
cv score: [0.62916667 0.62333333 0.69666667 0.77333333 0.7681962 ]
----------------------------------------
Trial 4611
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=145, random_state=100,
subsample=0.7))])
cv score: [0.51333333 0.4825 0.66833333 0.47875 0.42721519]
----------------------------------------
Trial 4612
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=185,
random_state=100))])
cv score: [0.75041667 0.58791667 0.80875 0.7925 0.80617089]
----------------------------------------
Trial 4613
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=46,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7075 0.69166667 0.78166667 0.78833333 0.83702532]
----------------------------------------
Trial 4614
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='sqrt',
n_estimators=13, random_state=100,
subsample=0.65))])
cv score: [0.66333333 0.68 0.67083333 0.84916667 0.82950949]
----------------------------------------
Trial 4615
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=150,
random_state=100))])
cv score: [0.6925 0.50125 0.72375 0.64958333 0.63251582]
----------------------------------------
Trial 4616
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=161,
random_state=100))])
cv score: [0.63583333 0.66916667 0.725 0.84333333 0.83781646]
----------------------------------------
Trial 4617
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=184,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79833333 0.66333333 0.78876582]
----------------------------------------
Trial 4618
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=121,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.635 0.64916667 0.71666667 0.72833333 0.73022152]
----------------------------------------
Trial 4619
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=171,
random_state=100))])
cv score: [0.56625 0.66458333 0.70333333 0.82291667 0.8125 ]
----------------------------------------
Trial 4620
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=165, random_state=100,
subsample=0.65))])
cv score: [0.72666667 0.66 0.78083333 0.83166667 0.82278481]
----------------------------------------
Trial 4621
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='log2',
n_estimators=172, random_state=100,
subsample=0.85))])
cv score: [0.60833333 0.655 0.69666667 0.81583333 0.80775316]
----------------------------------------
Trial 4622
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='log2',
n_estimators=28, random_state=100,
subsample=0.65))])
cv score: [0.36666667 0.53166667 0.5725 0.63583333 0.6028481 ]
----------------------------------------
Trial 4623
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68833333 0.65583333 0.78 0.765 0.81882911]
----------------------------------------
Trial 4624
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=57,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.57625 0.73875 0.72333333 0.8375 0.78560127]
----------------------------------------
Trial 4625
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=180, random_state=100,
subsample=0.85))])
cv score: [0.52166667 0.53583333 0.64416667 0.745 0.77531646]
----------------------------------------
Trial 4626
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=193, random_state=100))])
cv score: [0.685 0.62458333 0.74625 0.81416667 0.82120253]
----------------------------------------
Trial 4627
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=103,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70083333 0.65583333 0.73 0.76916667 0.8125 ]
----------------------------------------
Trial 4628
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.72666667 0.80083333 0.78416667 0.85759494]
----------------------------------------
Trial 4629
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=117,
random_state=100))])
cv score: [0.635 0.5825 0.73875 0.78 0.78401899]
----------------------------------------
Trial 4630
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=25,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74208333 0.73083333 0.78666667 0.8225 0.89003165]
----------------------------------------
Trial 4631
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=116, random_state=100,
subsample=0.75))])
cv score: [0.5775 0.45 0.755 0.59458333 0.5403481 ]
----------------------------------------
Trial 4632
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=83,
random_state=100))])
cv score: [0.68625 0.49375 0.8075 0.64666667 0.75237342]
----------------------------------------
Trial 4633
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=26,
random_state=100))])
cv score: [0.74333333 0.6425 0.69583333 0.775 0.78876582]
----------------------------------------
Trial 4634
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=87,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.665 0.67583333 0.70083333 0.77 0.81170886]
----------------------------------------
Trial 4635
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=187, random_state=100))])
cv score: [0.69666667 0.64916667 0.70916667 0.8 0.83544304]
----------------------------------------
Trial 4636
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=182, random_state=100))])
cv score: [0.67833333 0.6175 0.73 0.80583333 0.84256329]
----------------------------------------
Trial 4637
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=138,
random_state=100))])
cv score: [0.6675 0.59166667 0.72208333 0.78708333 0.77650316]
----------------------------------------
Trial 4638
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=170,
random_state=100))])
cv score: [0.76458333 0.49375 0.78708333 0.70208333 0.79232595]
----------------------------------------
Trial 4639
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=16,
random_state=100))])
cv score: [0.67666667 0.55041667 0.7 0.82958333 0.87183544]
----------------------------------------
Trial 4640
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=38, random_state=100,
subsample=0.7))])
cv score: [0.71083333 0.63333333 0.79333333 0.83166667 0.84335443]
----------------------------------------
Trial 4641
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='log2',
n_estimators=182, random_state=100,
subsample=0.65))])
cv score: [0.56666667 0.53416667 0.67583333 0.65666667 0.76898734]
----------------------------------------
Trial 4642
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
n_estimators=21, random_state=100,
subsample=0.85))])
cv score: [0.77458333 0.66958333 0.78875 0.84208333 0.86155063]
----------------------------------------
Trial 4643
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=18, random_state=100,
subsample=0.9))])
cv score: [0.70666667 0.61333333 0.66916667 0.68333333 0.63370253]
----------------------------------------
Trial 4644
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
n_estimators=69, random_state=100,
subsample=0.65))])
cv score: [0.7225 0.69833333 0.79166667 0.82833333 0.82832278]
----------------------------------------
Trial 4645
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=116, random_state=100,
subsample=0.6))])
cv score: [0.68416667 0.60333333 0.67 0.76666667 0.75870253]
----------------------------------------
Trial 4646
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=183, random_state=100,
subsample=0.85))])
cv score: [0.68333333 0.67166667 0.73166667 0.77666667 0.77689873]
----------------------------------------
Trial 4647
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=106, random_state=100,
subsample=0.85))])
cv score: [0.7275 0.59333333 0.7025 0.78166667 0.80300633]
----------------------------------------
Trial 4648
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=59,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.56666667 0.57333333 0.72833333 0.74333333 0.86471519]
----------------------------------------
Trial 4649
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=148, random_state=100))])
cv score: [0.66833333 0.66333333 0.73666667 0.8075 0.83860759]
----------------------------------------
Trial 4650
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=69,
random_state=100))])
cv score: [0.69 0.61583333 0.67083333 0.7775 0.81882911]
----------------------------------------
Trial 4651
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=135,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60041667 0.69708333 0.65666667 0.84083333 0.79786392]
----------------------------------------
Trial 4652
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='sqrt',
n_estimators=50, random_state=100,
subsample=0.6))])
cv score: [0.64666667 0.6675 0.71083333 0.72583333 0.76740506]
----------------------------------------
Trial 4653
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=154,
random_state=100))])
cv score: [0.53375 0.545 0.49708333 0.71333333 0.6028481 ]
----------------------------------------
Trial 4654
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=177,
random_state=100))])
cv score: [0.7525 0.60833333 0.78166667 0.8375 0.82199367]
----------------------------------------
Trial 4655
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=43, random_state=100,
subsample=0.85))])
cv score: [0.67583333 0.575 0.74166667 0.78916667 0.76186709]
----------------------------------------
Trial 4656
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='log2',
n_estimators=178,
random_state=100))])
cv score: [0.66583333 0.64333333 0.74083333 0.79833333 0.84968354]
----------------------------------------
Trial 4657
----------------------------------------
Parameters {'rf__n_estimators': 60, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=60, random_state=100))])
cv score: [0.66916667 0.60583333 0.70666667 0.77083333 0.83623418]
----------------------------------------
Trial 4658
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=13, random_state=100,
subsample=0.7))])
cv score: [0.71833333 0.6825 0.7475 0.70333333 0.56487342]
----------------------------------------
Trial 4659
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=58,
random_state=100))])
cv score: [0.72083333 0.6175 0.715 0.74333333 0.62816456]
----------------------------------------
Trial 4660
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='log2',
n_estimators=130, random_state=100,
subsample=0.7))])
cv score: [0.67833333 0.60916667 0.69 0.80833333 0.85443038]
----------------------------------------
Trial 4661
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=110, random_state=100,
subsample=0.7))])
cv score: [0.64666667 0.59583333 0.685 0.74583333 0.77689873]
----------------------------------------
Trial 4662
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
random_state=100))])
cv score: [0.77333333 0.69333333 0.7425 0.85833333 0.89082278]
----------------------------------------
Trial 4663
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=137,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63416667 0.6675 0.755 0.75833333 0.82753165]
----------------------------------------
Trial 4664
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='sqrt',
n_estimators=17, random_state=100,
subsample=0.65))])
cv score: [0.6475 0.68 0.67666667 0.78583333 0.7943038 ]
----------------------------------------
Trial 4665
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.9))])
cv score: [0.62333333 0.62583333 0.68166667 0.79416667 0.83544304]
----------------------------------------
Trial 4666
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
n_estimators=72, random_state=100,
subsample=0.9))])
cv score: [0.79916667 0.64041667 0.79166667 0.83208333 0.83306962]
----------------------------------------
Trial 4667
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=45,
random_state=100))])
cv score: [0.6425 0.60541667 0.75583333 0.74833333 0.81962025]
----------------------------------------
Trial 4668
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=65, random_state=100))])
cv score: [0.5675 0.71291667 0.69333333 0.79333333 0.81566456]
----------------------------------------
Trial 4669
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=49, random_state=100,
subsample=0.6))])
cv score: [0.70166667 0.66166667 0.7475 0.83666667 0.82832278]
----------------------------------------
Trial 4670
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68875 0.73833333 0.77416667 0.84166667 0.8528481 ]
----------------------------------------
Trial 4671
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=143,
random_state=100))])
cv score: [0.69083333 0.59583333 0.695 0.7925 0.81012658]
----------------------------------------
Trial 4672
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=49, random_state=100))])
cv score: [0.66333333 0.66833333 0.69416667 0.8275 0.85047468]
----------------------------------------
Trial 4673
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
n_estimators=25, random_state=100,
subsample=0.75))])
cv score: [0.62 0.65083333 0.7475 0.8025 0.70332278]
----------------------------------------
Trial 4674
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=146,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74583333 0.73583333 0.76708333 0.86 0.86392405]
----------------------------------------
Trial 4675
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=173,
random_state=100))])
cv score: [0.62333333 0.66833333 0.6975 0.8175 0.82832278]
----------------------------------------
Trial 4676
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=192, random_state=100,
subsample=0.75))])
cv score: [0.67666667 0.6375 0.7325 0.7425 0.69541139]
----------------------------------------
Trial 4677
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='log2',
n_estimators=70, random_state=100,
subsample=0.6))])
cv score: [0.66833333 0.63083333 0.74083333 0.81583333 0.74762658]
----------------------------------------
Trial 4678
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=183, random_state=100))])
cv score: [0.73666667 0.68375 0.72416667 0.85583333 0.88212025]
----------------------------------------
Trial 4679
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=64, random_state=100,
subsample=0.75))])
cv score: [0.64666667 0.65583333 0.69583333 0.84 0.85522152]
----------------------------------------
Trial 4680
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=145,
random_state=100,
subsample=0.95))])
cv score: [0.7425 0.63416667 0.81333333 0.8075 0.81091772]
----------------------------------------
Trial 4681
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=146,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75333333 0.7275 0.79333333 0.84333333 0.87658228]
----------------------------------------
Trial 4682
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=157,
random_state=100))])
cv score: [0.675 0.585 0.7175 0.81541667 0.82753165]
----------------------------------------
Trial 4683
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=175, random_state=100))])
cv score: [0.69583333 0.62916667 0.7175 0.81333333 0.83939873]
----------------------------------------
Trial 4684
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.605 0.55833333 0.76958333 0.72916667 0.73813291]
----------------------------------------
Trial 4685
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=149,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72583333 0.7525 0.78 0.8775 0.86471519]
----------------------------------------
Trial 4686
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=76, random_state=100,
subsample=0.8))])
cv score: [0.65416667 0.58916667 0.6525 0.74416667 0.78322785]
----------------------------------------
Trial 4687
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=180,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73583333 0.74833333 0.7825 0.86833333 0.875 ]
----------------------------------------
Trial 4688
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71916667 0.71416667 0.80916667 0.84166667 0.90664557]
----------------------------------------
Trial 4689
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=105,
random_state=100))])
cv score: [0.6375 0.58208333 0.73625 0.7775 0.78362342]
----------------------------------------
Trial 4690
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=153,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78083333 0.73083333 0.78166667 0.86916667 0.88449367]
----------------------------------------
Trial 4691
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='sqrt',
n_estimators=90, random_state=100,
subsample=0.65))])
cv score: [0.535 0.615 0.705 0.66916667 0.80537975]
----------------------------------------
Trial 4692
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=93, random_state=100,
subsample=0.65))])
cv score: [0.61083333 0.49833333 0.7625 0.74083333 0.75237342]
----------------------------------------
Trial 4693
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=169,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.735 0.70166667 0.78666667 0.82333333 0.87341772]
----------------------------------------
Trial 4694
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=83,
random_state=100))])
cv score: [0.72333333 0.60833333 0.70166667 0.80166667 0.82594937]
----------------------------------------
Trial 4695
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=67,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74833333 0.72041667 0.7775 0.87 0.90031646]
----------------------------------------
Trial 4696
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=138,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65 0.68 0.78333333 0.79416667 0.86313291]
----------------------------------------
Trial 4697
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=38,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70583333 0.735 0.79583333 0.87666667 0.90308544]
----------------------------------------
Trial 4698
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=137,
random_state=100))])
cv score: [0.55791667 0.67041667 0.69416667 0.82708333 0.80458861]
----------------------------------------
Trial 4699
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=2, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=198,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61958333 0.7075 0.64541667 0.77416667 0.72191456]
----------------------------------------
Trial 4700
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=119,
random_state=100,
subsample=0.85))])
cv score: [0.705 0.6775 0.74333333 0.82 0.85443038]
----------------------------------------
Trial 4701
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=155, random_state=100))])
cv score: [0.70916667 0.645 0.73083333 0.79958333 0.83148734]
----------------------------------------
Trial 4702
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='log2',
n_estimators=11, random_state=100,
subsample=0.8))])
cv score: [0.57416667 0.6375 0.725 0.76916667 0.76344937]
----------------------------------------
Trial 4703
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=155, random_state=100,
subsample=0.8))])
cv score: [0.6475 0.565 0.6425 0.71166667 0.65981013]
----------------------------------------
Trial 4704
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=88,
random_state=100))])
cv score: [0.68541667 0.49375 0.80833333 0.64625 0.75316456]
----------------------------------------
Trial 4705
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=12, random_state=100))])
cv score: [0.68833333 0.64208333 0.72666667 0.85041667 0.83306962]
----------------------------------------
Trial 4706
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=129,
random_state=100))])
cv score: [0.50958333 0.67541667 0.70125 0.81 0.76186709]
----------------------------------------
Trial 4707
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='sqrt',
n_estimators=35, random_state=100,
subsample=0.75))])
cv score: [0.5975 0.58416667 0.6575 0.82416667 0.78243671]
----------------------------------------
Trial 4708
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68416667 0.6775 0.77666667 0.775 0.84731013]
----------------------------------------
Trial 4709
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=24, random_state=100,
subsample=0.75))])
cv score: [0.70416667 0.66041667 0.82125 0.83166667 0.82753165]
----------------------------------------
Trial 4710
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=163, random_state=100,
subsample=0.7))])
cv score: [0.55083333 0.5025 0.74583333 0.74166667 0.70648734]
----------------------------------------
Trial 4711
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=27,
random_state=100))])
cv score: [0.71333333 0.64125 0.73916667 0.78833333 0.85047468]
----------------------------------------
Trial 4712
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=70,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67083333 0.66833333 0.71916667 0.72 0.75158228]
----------------------------------------
Trial 4713
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='log2',
n_estimators=132, random_state=100,
subsample=0.85))])
cv score: [0.62083333 0.6625 0.6775 0.7375 0.70332278]
----------------------------------------
Trial 4714
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.9))])
cv score: [0.61333333 0.57583333 0.69583333 0.75166667 0.77373418]
----------------------------------------
Trial 4715
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=22, random_state=100))])
cv score: [0.68791667 0.68458333 0.69375 0.85291667 0.88053797]
----------------------------------------
Trial 4716
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='log2',
n_estimators=167, random_state=100,
subsample=0.7))])
cv score: [0.64583333 0.59333333 0.6575 0.71416667 0.73813291]
----------------------------------------
Trial 4717
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.85))])
cv score: [0.67333333 0.57583333 0.6875 0.79083333 0.83781646]
----------------------------------------
Trial 4718
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
n_estimators=194, random_state=100,
subsample=0.65))])
cv score: [0.77583333 0.68583333 0.75833333 0.85833333 0.87974684]
----------------------------------------
Trial 4719
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=61, random_state=100))])
cv score: [0.74 0.66541667 0.77333333 0.83125 0.84177215]
----------------------------------------
Trial 4720
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=39,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.63583333 0.745 0.80333333 0.82436709]
----------------------------------------
Trial 4721
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=45, random_state=100))])
cv score: [0.65583333 0.67833333 0.7 0.78416667 0.83781646]
----------------------------------------
Trial 4722
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=51,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67916667 0.74791667 0.77416667 0.84916667 0.84889241]
----------------------------------------
Trial 4723
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=114, random_state=100,
subsample=0.75))])
cv score: [0.63083333 0.63708333 0.60916667 0.5325 0.63132911]
----------------------------------------
Trial 4724
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=158,
random_state=100))])
cv score: [0.58666667 0.66083333 0.70333333 0.8325 0.82594937]
----------------------------------------
Trial 4725
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=130,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61 0.595 0.71166667 0.7725 0.73338608]
----------------------------------------
Trial 4726
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=73,
random_state=100))])
cv score: [0.68416667 0.615 0.69583333 0.7525 0.78718354]
----------------------------------------
Trial 4727
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=183, random_state=100,
subsample=0.9))])
cv score: [0.71333333 0.66208333 0.81666667 0.84 0.81803797]
----------------------------------------
Trial 4728
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=20, random_state=100))])
cv score: [0.65625 0.58625 0.70833333 0.73541667 0.75593354]
----------------------------------------
Trial 4729
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=52, random_state=100))])
cv score: [0.66583333 0.61916667 0.7 0.7825 0.84572785]
----------------------------------------
Trial 4730
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=10,
random_state=100))])
cv score: [0.6525 0.60208333 0.74916667 0.78375 0.80181962]
----------------------------------------
Trial 4731
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=118, random_state=100))])
cv score: [0.68583333 0.63833333 0.7325 0.81583333 0.83306962]
----------------------------------------
Trial 4732
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=106, random_state=100,
subsample=0.7))])
cv score: [0.51333333 0.455 0.645 0.47875 0.42721519]
----------------------------------------
Trial 4733
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74416667 0.73875 0.7875 0.86083333 0.8931962 ]
----------------------------------------
Trial 4734
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='log2',
n_estimators=77, random_state=100,
subsample=0.75))])
cv score: [0.68583333 0.615 0.69083333 0.81916667 0.77768987]
----------------------------------------
Trial 4735
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=114, random_state=100))])
cv score: [0.615 0.69916667 0.7025 0.79416667 0.82674051]
----------------------------------------
Trial 4736
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=50, random_state=100))])
cv score: [0.655 0.68083333 0.72 0.78583333 0.83148734]
----------------------------------------
Trial 4737
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=14, random_state=100,
subsample=0.65))])
cv score: [0.51833333 0.63583333 0.63166667 0.56333333 0.55379747]
----------------------------------------
Trial 4738
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=151, random_state=100,
subsample=0.6))])
cv score: [0.705 0.665 0.78 0.82333333 0.82120253]
----------------------------------------
Trial 4739
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=154, random_state=100,
subsample=0.9))])
cv score: [0.6675 0.57916667 0.68666667 0.76166667 0.77610759]
----------------------------------------
Trial 4740
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=143, random_state=100))])
cv score: [0.63 0.69166667 0.70583333 0.79916667 0.8306962 ]
----------------------------------------
Trial 4741
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=90,
random_state=100))])
cv score: [0.63875 0.59666667 0.73208333 0.77875 0.78362342]
----------------------------------------
Trial 4742
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=71,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67 0.70333333 0.7875 0.79916667 0.88844937]
----------------------------------------
Trial 4743
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
n_estimators=183, random_state=100,
subsample=0.85))])
cv score: [0.72916667 0.66 0.81416667 0.825 0.8164557 ]
----------------------------------------
Trial 4744
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=50,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71 0.67 0.7775 0.83833333 0.90901899]
----------------------------------------
Trial 4745
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=61,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69875 0.73833333 0.73916667 0.84291667 0.84414557]
----------------------------------------
Trial 4746
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=118, random_state=100,
subsample=0.9))])
cv score: [0.7575 0.61666667 0.82833333 0.84 0.77848101]
----------------------------------------
Trial 4747
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=192, random_state=100,
subsample=0.75))])
cv score: [0.71083333 0.68416667 0.77166667 0.8 0.85205696]
----------------------------------------
Trial 4748
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=32,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65458333 0.74333333 0.76791667 0.85166667 0.87341772]
----------------------------------------
Trial 4749
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=138, random_state=100))])
cv score: [0.73666667 0.67958333 0.77958333 0.83791667 0.8346519 ]
----------------------------------------
Trial 4750
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=59,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.755 0.71666667 0.76333333 0.8175 0.85996835]
----------------------------------------
Trial 4751
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74333333 0.69666667 0.77583333 0.81416667 0.85996835]
----------------------------------------
Trial 4752
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53583333 0.72833333 0.74166667 0.77708333 0.8568038 ]
----------------------------------------
Trial 4753
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=10,
random_state=100))])
cv score: [0.51666667 0.59666667 0.64333333 0.85416667 0.81131329]
----------------------------------------
Trial 4754
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=136, random_state=100))])
cv score: [0.725 0.68541667 0.78333333 0.83416667 0.83386076]
----------------------------------------
Trial 4755
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76708333 0.72166667 0.74291667 0.87125 0.86431962]
----------------------------------------
Trial 4756
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
n_estimators=80, random_state=100,
subsample=0.9))])
cv score: [0.72416667 0.63416667 0.75833333 0.74083333 0.73259494]
----------------------------------------
Trial 4757
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=15, random_state=100,
subsample=0.9))])
cv score: [0.6825 0.61666667 0.71083333 0.7325 0.76028481]
----------------------------------------
Trial 4758
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=190, random_state=100,
subsample=0.65))])
cv score: [0.695 0.7075 0.72833333 0.8175 0.83939873]
----------------------------------------
Trial 4759
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=102, random_state=100,
subsample=0.85))])
cv score: [0.65166667 0.63833333 0.68083333 0.795 0.81962025]
----------------------------------------
Trial 4760
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=182,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62416667 0.66083333 0.70833333 0.72083333 0.7943038 ]
----------------------------------------
Trial 4761
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=58,
random_state=100))])
cv score: [0.6075 0.56375 0.6025 0.77625 0.78797468]
----------------------------------------
Trial 4762
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=105,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69583333 0.69166667 0.78666667 0.80083333 0.8710443 ]
----------------------------------------
Trial 4763
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=88,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77666667 0.71083333 0.80083333 0.84 0.87974684]
----------------------------------------
Trial 4764
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=54,
random_state=100))])
cv score: [0.6775 0.60125 0.7 0.80583333 0.84572785]
----------------------------------------
Trial 4765
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
n_estimators=97, random_state=100,
subsample=0.95))])
cv score: [0.70083333 0.66 0.72416667 0.815 0.78401899]
----------------------------------------
Trial 4766
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=159, random_state=100,
subsample=0.85))])
cv score: [0.73916667 0.73083333 0.77375 0.85166667 0.89952532]
----------------------------------------
Trial 4767
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=101, random_state=100,
subsample=0.85))])
cv score: [0.66333333 0.61666667 0.7675 0.69833333 0.74683544]
----------------------------------------
Trial 4768
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=77, random_state=100,
subsample=0.8))])
cv score: [0.58166667 0.62333333 0.66916667 0.70416667 0.77056962]
----------------------------------------
Trial 4769
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, n_estimators=22,
random_state=100, subsample=0.7))])
cv score: [0.68166667 0.675 0.7625 0.83333333 0.83939873]
----------------------------------------
Trial 4770
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
n_estimators=54, random_state=100,
subsample=0.8))])
cv score: [0.60583333 0.675 0.61125 0.86375 0.77056962]
----------------------------------------
Trial 4771
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=152, random_state=100))])
cv score: [0.6825 0.66083333 0.72166667 0.8075 0.85917722]
----------------------------------------
Trial 4772
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=108,
random_state=100))])
cv score: [0.66833333 0.50541667 0.79916667 0.66125 0.78797468]
----------------------------------------
Trial 4773
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=111, random_state=100))])
cv score: [0.68708333 0.63333333 0.74458333 0.82375 0.81566456]
----------------------------------------
Trial 4774
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=106,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83625 0.82199367]
----------------------------------------
Trial 4775
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=140,
random_state=100))])
cv score: [0.68666667 0.62833333 0.68833333 0.795 0.82436709]
----------------------------------------
Trial 4776
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, n_estimators=198,
random_state=100,
subsample=0.65))])
cv score: [0.6725 0.67666667 0.71166667 0.77 0.66693038]
----------------------------------------
Trial 4777
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72916667 0.70916667 0.78333333 0.81833333 0.89556962]
----------------------------------------
Trial 4778
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=193,
random_state=100))])
cv score: [0.6775 0.65 0.71833333 0.81666667 0.84889241]
----------------------------------------
Trial 4779
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=140, random_state=100))])
cv score: [0.67833333 0.62166667 0.75083333 0.82208333 0.82278481]
----------------------------------------
Trial 4780
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=88, random_state=100))])
cv score: [0.69666667 0.66583333 0.7275 0.7925 0.86471519]
----------------------------------------
Trial 4781
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=123, random_state=100))])
cv score: [0.72041667 0.67958333 0.785 0.83416667 0.83030063]
----------------------------------------
Trial 4782
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=45, random_state=100,
subsample=0.6))])
cv score: [0.63 0.59 0.6325 0.7825 0.79351266]
----------------------------------------
Trial 4783
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=66, random_state=100,
subsample=0.7))])
cv score: [0.72583333 0.68416667 0.79666667 0.815 0.83781646]
----------------------------------------
Trial 4784
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=165,
random_state=100))])
cv score: [0.68833333 0.62666667 0.6975 0.805 0.83544304]
----------------------------------------
Trial 4785
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=10, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.705 0.72 0.77125 0.86333333 0.85363924]
----------------------------------------
Trial 4786
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='log2',
n_estimators=85, random_state=100,
subsample=0.75))])
cv score: [0.675 0.63166667 0.72583333 0.805 0.8085443 ]
----------------------------------------
Trial 4787
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=144, random_state=100))])
cv score: [0.66916667 0.665 0.74083333 0.80333333 0.83781646]
----------------------------------------
Trial 4788
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=29, random_state=100))])
cv score: [0.68166667 0.59 0.74916667 0.7975 0.84889241]
----------------------------------------
Trial 4789
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=11,
random_state=100))])
cv score: [0.65166667 0.62791667 0.72 0.77375 0.78164557]
----------------------------------------
Trial 4790
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=114,
random_state=100))])
cv score: [0.68708333 0.49375 0.81 0.64791667 0.75316456]
----------------------------------------
Trial 4791
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=81,
random_state=100, subsample=0.7))])
cv score: [0.7 0.68916667 0.76583333 0.83833333 0.82594937]
----------------------------------------
Trial 4792
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=67, random_state=100,
subsample=0.85))])
cv score: [0.70333333 0.645 0.715 0.81583333 0.82436709]
----------------------------------------
Trial 4793
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=43, random_state=100))])
cv score: [0.73083333 0.66 0.77541667 0.83458333 0.82911392]
----------------------------------------
Trial 4794
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=97,
random_state=100))])
cv score: [0.6925 0.675 0.715 0.75333333 0.64477848]
----------------------------------------
Trial 4795
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=184, random_state=100,
subsample=0.75))])
cv score: [0.59416667 0.6575 0.64958333 0.70083333 0.59177215]
----------------------------------------
Trial 4796
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='sqrt',
n_estimators=57, random_state=100,
subsample=0.6))])
cv score: [0.64416667 0.6125 0.625 0.62583333 0.57911392]
----------------------------------------
Trial 4797
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=118,
random_state=100))])
cv score: [0.61583333 0.6725 0.69333333 0.81333333 0.83148734]
----------------------------------------
Trial 4798
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=173, random_state=100))])
cv score: [0.725 0.67541667 0.77583333 0.83666667 0.82753165]
----------------------------------------
Trial 4799
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='sqrt',
n_estimators=36, random_state=100,
subsample=0.65))])
cv score: [0.61416667 0.59416667 0.66833333 0.78916667 0.82120253]
----------------------------------------
Trial 4800
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=118, random_state=100))])
cv score: [0.65666667 0.6575 0.70333333 0.82416667 0.85601266]
----------------------------------------
Trial 4801
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=10,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69458333 0.725 0.80625 0.78166667 0.90506329]
----------------------------------------
Trial 4802
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=3, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=18,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.53416667 0.7325 0.73083333 0.83833333 0.81724684]
----------------------------------------
Trial 4803
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69083333 0.66583333 0.795 0.825 0.87737342]
----------------------------------------
Trial 4804
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73416667 0.69833333 0.7925 0.8275 0.86629747]
----------------------------------------
Trial 4805
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=86, random_state=100))])
cv score: [0.73208333 0.66541667 0.78125 0.83 0.83148734]
----------------------------------------
Trial 4806
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=188, random_state=100))])
cv score: [0.69333333 0.61083333 0.73 0.82208333 0.82278481]
----------------------------------------
Trial 4807
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=176,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69666667 0.6975 0.78083333 0.81666667 0.86708861]
----------------------------------------
Trial 4808
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
n_estimators=38, random_state=100,
subsample=0.9))])
cv score: [0.79875 0.66166667 0.76833333 0.86541667 0.83939873]
----------------------------------------
Trial 4809
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=139, random_state=100))])
cv score: [0.775 0.67916667 0.75416667 0.85083333 0.89873418]
----------------------------------------
Trial 4810
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=74, random_state=100))])
cv score: [0.72458333 0.67083333 0.7775 0.83083333 0.83623418]
----------------------------------------
Trial 4811
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.9))])
cv score: [0.61666667 0.57583333 0.67833333 0.78583333 0.70648734]
----------------------------------------
Trial 4812
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='sqrt',
n_estimators=189, random_state=100,
subsample=0.95))])
cv score: [0.52333333 0.55416667 0.69 0.70583333 0.73655063]
----------------------------------------
Trial 4813
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=22,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.66208333 0.73041667 0.78875 0.8425 0.82674051]
----------------------------------------
Trial 4814
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=67,
random_state=100))])
cv score: [0.69333333 0.62 0.6725 0.7775 0.82041139]
----------------------------------------
Trial 4815
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=156, random_state=100))])
cv score: [0.70083333 0.63 0.74 0.78833333 0.81724684]
----------------------------------------
Trial 4816
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=145, random_state=100,
subsample=0.85))])
cv score: [0.67833333 0.57166667 0.70583333 0.77666667 0.75949367]
----------------------------------------
Trial 4817
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=192,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6275 0.6525 0.7475 0.75333333 0.83860759]
----------------------------------------
Trial 4818
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=121, random_state=100))])
cv score: [0.69333333 0.62416667 0.74 0.795 0.82674051]
----------------------------------------
Trial 4819
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=17,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71291667 0.72916667 0.775 0.83416667 0.86471519]
----------------------------------------
Trial 4820
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=21, random_state=100,
subsample=0.7))])
cv score: [0.5475 0.56208333 0.6475 0.66333333 0.60522152]
----------------------------------------
Trial 4821
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=65, random_state=100))])
cv score: [0.6425 0.62333333 0.76416667 0.8075 0.82911392]
----------------------------------------
Trial 4822
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=64,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.79416667 0.75375 0.795 0.85583333 0.85443038]
----------------------------------------
Trial 4823
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=62, random_state=100,
subsample=0.9))])
cv score: [0.635 0.6425 0.70166667 0.84 0.79193038]
----------------------------------------
Trial 4824
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=159,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 4825
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=139,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.705 0.68083333 0.7875 0.79416667 0.87420886]
----------------------------------------
Trial 4826
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=199,
random_state=100))])
cv score: [0.6375 0.57625 0.7275 0.795 0.80063291]
----------------------------------------
Trial 4827
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=12, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=168,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.74416667 0.72125 0.78375 0.83916667 0.86550633]
----------------------------------------
Trial 4828
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=141,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72875 0.7025 0.76208333 0.86333333 0.87341772]
----------------------------------------
Trial 4829
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=45,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76666667 0.615 0.735 0.82666667 0.79588608]
----------------------------------------
Trial 4830
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=108,
random_state=100,
subsample=0.95))])
cv score: [0.72 0.70583333 0.75208333 0.795 0.83623418]
----------------------------------------
Trial 4831
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=137,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77416667 0.74291667 0.7775 0.85416667 0.84889241]
----------------------------------------
Trial 4832
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=39,
random_state=100))])
cv score: [0.68958333 0.48916667 0.77916667 0.67166667 0.63726266]
----------------------------------------
Trial 4833
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='log2',
n_estimators=168, random_state=100,
subsample=0.75))])
cv score: [0.65333333 0.67583333 0.67833333 0.76666667 0.70648734]
----------------------------------------
Trial 4834
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='log2',
n_estimators=125,
random_state=100))])
cv score: [0.575 0.55916667 0.70416667 0.68583333 0.78560127]
----------------------------------------
Trial 4835
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=50,
random_state=100))])
cv score: [0.7075 0.6525 0.6625 0.79166667 0.80537975]
----------------------------------------
Trial 4836
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=135, random_state=100))])
cv score: [0.67666667 0.64666667 0.7275 0.81583333 0.84256329]
----------------------------------------
Trial 4837
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=189, random_state=100))])
cv score: [0.70833333 0.62916667 0.72833333 0.79666667 0.82911392]
----------------------------------------
Trial 4838
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=80,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64166667 0.65333333 0.79083333 0.77166667 0.82832278]
----------------------------------------
Trial 4839
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=20, random_state=100))])
cv score: [0.77333333 0.69416667 0.69416667 0.85875 0.88132911]
----------------------------------------
Trial 4840
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69916667 0.72333333 0.80083333 0.85333333 0.88765823]
----------------------------------------
Trial 4841
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=37, random_state=100))])
cv score: [0.72625 0.69458333 0.7025 0.85291667 0.87183544]
----------------------------------------
Trial 4842
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=197, random_state=100))])
cv score: [0.76583333 0.68583333 0.78166667 0.84333333 0.85047468]
----------------------------------------
Trial 4843
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64666667 0.60833333 0.73916667 0.67583333 0.68591772]
----------------------------------------
Trial 4844
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=14,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68333333 0.68583333 0.78416667 0.80916667 0.85996835]
----------------------------------------
Trial 4845
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='sqrt',
n_estimators=182, random_state=100,
subsample=0.6))])
cv score: [0.64708333 0.5175 0.58416667 0.54166667 0.73813291]
----------------------------------------
Trial 4846
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=118,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68916667 0.66166667 0.78416667 0.78666667 0.85917722]
----------------------------------------
Trial 4847
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=184, random_state=100,
subsample=0.8))])
cv score: [0.42333333 0.63333333 0.70083333 0.5125 0.47626582]
----------------------------------------
Trial 4848
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=22,
random_state=100))])
cv score: [0.69583333 0.66708333 0.67875 0.79708333 0.83227848]
----------------------------------------
Trial 4849
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=13, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=44,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68666667 0.72958333 0.73583333 0.84583333 0.82674051]
----------------------------------------
Trial 4850
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=193,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69166667 0.68666667 0.78333333 0.77333333 0.83306962]
----------------------------------------
Trial 4851
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=110, random_state=100))])
cv score: [0.67416667 0.64166667 0.72416667 0.80416667 0.84731013]
----------------------------------------
Trial 4852
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=144,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.775 0.72083333 0.80416667 0.86333333 0.88132911]
----------------------------------------
Trial 4853
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=59, random_state=100))])
cv score: [0.65291667 0.63916667 0.72458333 0.81291667 0.8125 ]
----------------------------------------
Trial 4854
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=84,
random_state=100))])
cv score: [0.63833333 0.59291667 0.73166667 0.75916667 0.77768987]
----------------------------------------
Trial 4855
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=12, random_state=100,
subsample=0.95))])
cv score: [0.64416667 0.5925 0.695 0.67333333 0.88212025]
----------------------------------------
Trial 4856
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=128,
random_state=100))])
cv score: [0.66916667 0.58583333 0.70083333 0.8025 0.81724684]
----------------------------------------
Trial 4857
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=64,
random_state=100))])
cv score: [0.69666667 0.64041667 0.71166667 0.78083333 0.84810127]
----------------------------------------
Trial 4858
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=16,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.47083333 0.70375 0.63333333 0.78666667 0.8215981 ]
----------------------------------------
Trial 4859
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=151, random_state=100,
subsample=0.75))])
cv score: [0.62333333 0.6075 0.62583333 0.68166667 0.69224684]
----------------------------------------
Trial 4860
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=135,
random_state=100))])
cv score: [0.6975 0.60083333 0.67833333 0.8075 0.83939873]
----------------------------------------
Trial 4861
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=60, random_state=100,
subsample=0.6))])
cv score: [0.7625 0.62916667 0.69083333 0.765 0.87025316]
----------------------------------------
Trial 4862
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6575 0.61416667 0.725 0.7225 0.73101266]
----------------------------------------
Trial 4863
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=90,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62916667 0.63 0.78666667 0.74666667 0.74762658]
----------------------------------------
Trial 4864
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=43,
random_state=100))])
cv score: [0.68041667 0.51375 0.82458333 0.665 0.66416139]
----------------------------------------
Trial 4865
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=102, random_state=100))])
cv score: [0.69916667 0.62916667 0.7225 0.81083333 0.82753165]
----------------------------------------
Trial 4866
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='log2',
n_estimators=159, random_state=100,
subsample=0.65))])
cv score: [0.61833333 0.67 0.71416667 0.8325 0.80696203]
----------------------------------------
Trial 4867
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=190,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67 0.6725 0.77833333 0.76916667 0.86075949]
----------------------------------------
Trial 4868
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=153,
random_state=100))])
cv score: [0.76458333 0.49375 0.78708333 0.70291667 0.79232595]
----------------------------------------
Trial 4869
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=116,
random_state=100))])
cv score: [0.695 0.60083333 0.6775 0.80583333 0.83623418]
----------------------------------------
Trial 4870
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=35,
random_state=100))])
cv score: [0.53333333 0.67125 0.72416667 0.84083333 0.80893987]
----------------------------------------
Trial 4871
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
n_estimators=169, random_state=100,
subsample=0.6))])
cv score: [0.74333333 0.67583333 0.78166667 0.8425 0.82199367]
----------------------------------------
Trial 4872
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=132, random_state=100))])
cv score: [0.5675 0.71833333 0.69 0.795 0.78481013]
----------------------------------------
Trial 4873
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=11, random_state=100))])
cv score: [0.77708333 0.68375 0.67583333 0.84875 0.86234177]
----------------------------------------
Trial 4874
----------------------------------------
Parameters {'rf__n_estimators': 125, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=125,
random_state=100))])
cv score: [0.64458333 0.5825 0.74166667 0.78708333 0.77452532]
----------------------------------------
Trial 4875
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=179,
random_state=100))])
cv score: [0.67916667 0.61916667 0.6975 0.80083333 0.83623418]
----------------------------------------
Trial 4876
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='sqrt',
n_estimators=38,
random_state=100))])
cv score: [0.72 0.63166667 0.71833333 0.79333333 0.82674051]
----------------------------------------
Trial 4877
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=190, random_state=100,
subsample=0.75))])
cv score: [0.715 0.6275 0.7025 0.78666667 0.75632911]
----------------------------------------
Trial 4878
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=48, random_state=100,
subsample=0.85))])
cv score: [0.7 0.62166667 0.83 0.83083333 0.75949367]
----------------------------------------
Trial 4879
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=32, random_state=100,
subsample=0.65))])
cv score: [0.685 0.58083333 0.7025 0.7875 0.8568038 ]
----------------------------------------
Trial 4880
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60916667 0.64416667 0.7525 0.7225 0.75316456]
----------------------------------------
Trial 4881
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=104,
random_state=100))])
cv score: [0.75333333 0.60833333 0.78166667 0.83666667 0.82199367]
----------------------------------------
Trial 4882
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=194,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6275 0.56916667 0.76666667 0.71541667 0.72943038]
----------------------------------------
Trial 4883
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=28,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76333333 0.73916667 0.77708333 0.8375 0.88370253]
----------------------------------------
Trial 4884
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=180, random_state=100,
subsample=0.6))])
cv score: [0.72333333 0.65166667 0.7225 0.79 0.8306962 ]
----------------------------------------
Trial 4885
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=65, random_state=100))])
cv score: [0.5675 0.71291667 0.69333333 0.79333333 0.81566456]
----------------------------------------
Trial 4886
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=191,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73 0.6725 0.8 0.82833333 0.88053797]
----------------------------------------
Trial 4887
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=145, random_state=100,
subsample=0.9))])
cv score: [0.74083333 0.64 0.83166667 0.83583333 0.79746835]
----------------------------------------
Trial 4888
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='sqrt',
n_estimators=130, random_state=100,
subsample=0.95))])
cv score: [0.68083333 0.625 0.70666667 0.80083333 0.82911392]
----------------------------------------
Trial 4889
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=40, random_state=100,
subsample=0.6))])
cv score: [0.705 0.70583333 0.80083333 0.83 0.79905063]
----------------------------------------
Trial 4890
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=135,
random_state=100))])
cv score: [0.51541667 0.68875 0.70958333 0.825 0.77452532]
----------------------------------------
Trial 4891
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=72, random_state=100,
subsample=0.9))])
cv score: [0.75166667 0.635 0.80916667 0.82666667 0.81724684]
----------------------------------------
Trial 4892
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=4, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=71,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69333333 0.73416667 0.73166667 0.85916667 0.81962025]
----------------------------------------
Trial 4893
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71416667 0.68166667 0.78083333 0.75666667 0.85759494]
----------------------------------------
Trial 4894
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=86,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78333333 0.73875 0.80416667 0.85041667 0.86946203]
----------------------------------------
Trial 4895
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=62, random_state=100))])
cv score: [0.62083333 0.65833333 0.735 0.78666667 0.82990506]
----------------------------------------
Trial 4896
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=144, random_state=100,
subsample=0.65))])
cv score: [0.79416667 0.68333333 0.77583333 0.84 0.87579114]
----------------------------------------
Trial 4897
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=85, random_state=100))])
cv score: [0.7325 0.68333333 0.78333333 0.82833333 0.85126582]
----------------------------------------
Trial 4898
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=59, random_state=100,
subsample=0.75))])
cv score: [0.665 0.6575 0.67333333 0.80166667 0.83702532]
----------------------------------------
Trial 4899
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=8, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71166667 0.7375 0.76291667 0.86166667 0.85363924]
----------------------------------------
Trial 4900
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='log2',
n_estimators=70, random_state=100,
subsample=0.75))])
cv score: [0.7225 0.655 0.735 0.78 0.85443038]
----------------------------------------
Trial 4901
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=53,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67166667 0.6675 0.74666667 0.79416667 0.78481013]
----------------------------------------
Trial 4902
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
n_estimators=131, random_state=100,
subsample=0.85))])
cv score: [0.63583333 0.63666667 0.71083333 0.77583333 0.71202532]
----------------------------------------
Trial 4903
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=182,
random_state=100))])
cv score: [0.68083333 0.60416667 0.70083333 0.79 0.79984177]
----------------------------------------
Trial 4904
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=36,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.64416667 0.69333333 0.7325 0.76833333 0.77056962]
----------------------------------------
Trial 4905
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=126,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.5975 0.67916667 0.7525 0.77333333 0.8528481 ]
----------------------------------------
Trial 4906
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=154,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72833333 0.69416667 0.8125 0.8275 0.91297468]
----------------------------------------
Trial 4907
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=40, random_state=100,
subsample=0.8))])
cv score: [0.6975 0.60833333 0.72083333 0.78833333 0.74841772]
----------------------------------------
Trial 4908
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=126,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78666667 0.715 0.76541667 0.86 0.88686709]
----------------------------------------
Trial 4909
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=9, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=97,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.795 0.73708333 0.80125 0.83791667 0.86234177]
----------------------------------------
Trial 4910
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=13,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=81,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.62666667 0.635 0.71833333 0.73333333 0.83386076]
----------------------------------------
Trial 4911
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='log2',
n_estimators=67, random_state=100,
subsample=0.9))])
cv score: [0.63083333 0.57666667 0.7 0.74583333 0.78085443]
----------------------------------------
Trial 4912
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=107, random_state=100))])
cv score: [0.555 0.72 0.68666667 0.79 0.78560127]
----------------------------------------
Trial 4913
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=191, random_state=100,
subsample=0.65))])
cv score: [0.66166667 0.58916667 0.66 0.71 0.73971519]
----------------------------------------
Trial 4914
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=189,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70416667 0.67166667 0.76333333 0.80166667 0.85996835]
----------------------------------------
Trial 4915
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=37,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.655 0.67166667 0.75333333 0.77666667 0.88844937]
----------------------------------------
Trial 4916
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=69,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77416667 0.68166667 0.77875 0.84 0.84098101]
----------------------------------------
Trial 4917
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=188,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.7775 0.72125 0.78583333 0.8425 0.86550633]
----------------------------------------
Trial 4918
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=120, random_state=100))])
cv score: [0.73083333 0.68875 0.73416667 0.86 0.87737342]
----------------------------------------
Trial 4919
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=173, random_state=100,
subsample=0.6))])
cv score: [0.705 0.6175 0.71416667 0.80333333 0.81962025]
----------------------------------------
Trial 4920
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=136,
random_state=100))])
cv score: [0.6925 0.4875 0.72 0.6675 0.63528481]
----------------------------------------
Trial 4921
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=116,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.72 0.675 0.79416667 0.7925 0.86155063]
----------------------------------------
Trial 4922
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=158, random_state=100,
subsample=0.95))])
cv score: [0.66 0.61666667 0.66583333 0.7925 0.76661392]
----------------------------------------
Trial 4923
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=191,
random_state=100))])
cv score: [0.81375 0.58166667 0.6625 0.84416667 0.74208861]
----------------------------------------
Trial 4924
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=47, random_state=100,
subsample=0.95))])
cv score: [0.6775 0.5875 0.69666667 0.7675 0.78481013]
----------------------------------------
Trial 4925
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=9,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=20,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68333333 0.68833333 0.76583333 0.76916667 0.79351266]
----------------------------------------
Trial 4926
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.03,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=85,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77833333 0.715 0.79083333 0.845 0.90427215]
----------------------------------------
Trial 4927
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=24,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.76166667 0.725 0.74333333 0.79 0.85601266]
----------------------------------------
Trial 4928
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=8,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=13,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67 0.66916667 0.735 0.75416667 0.84256329]
----------------------------------------
Trial 4929
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=185,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6875 0.6725 0.7625 0.76416667 0.875 ]
----------------------------------------
Trial 4930
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=6,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=96,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.60166667 0.6375 0.77833333 0.77833333 0.84651899]
----------------------------------------
Trial 4931
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='log2',
n_estimators=76, random_state=100,
subsample=0.6))])
cv score: [0.72916667 0.63333333 0.71666667 0.77916667 0.82832278]
----------------------------------------
Trial 4932
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=20,
random_state=100))])
cv score: [0.70458333 0.5925 0.66583333 0.76666667 0.8255538 ]
----------------------------------------
Trial 4933
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=71,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.61833333 0.66583333 0.7575 0.77666667 0.81962025]
----------------------------------------
Trial 4934
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=97,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.715 0.68416667 0.77333333 0.8125 0.87341772]
----------------------------------------
Trial 4935
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=49, random_state=100))])
cv score: [0.67 0.61333333 0.69916667 0.7775 0.85838608]
----------------------------------------
Trial 4936
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=42, random_state=100,
subsample=0.75))])
cv score: [0.6775 0.67333333 0.685 0.81083333 0.78876582]
----------------------------------------
Trial 4937
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='log2',
n_estimators=90, random_state=100,
subsample=0.75))])
cv score: [0.67666667 0.635 0.73833333 0.77833333 0.77768987]
----------------------------------------
Trial 4938
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='log2',
n_estimators=109, random_state=100,
subsample=0.75))])
cv score: [0.695 0.61416667 0.68 0.82416667 0.78955696]
----------------------------------------
Trial 4939
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=2,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=128,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.6625 0.62583333 0.69 0.73416667 0.71598101]
----------------------------------------
Trial 4940
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=3,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=199,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77833333 0.63416667 0.75083333 0.78666667 0.82041139]
----------------------------------------
Trial 4941
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=167, random_state=100))])
cv score: [0.67083333 0.655 0.73666667 0.79583333 0.84651899]
----------------------------------------
Trial 4942
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=7, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=174,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.7, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.70916667 0.71916667 0.76166667 0.86666667 0.85996835]
----------------------------------------
Trial 4943
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=21,
random_state=100))])
cv score: [0.69083333 0.67125 0.67166667 0.79875 0.84810127]
----------------------------------------
Trial 4944
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.1, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.01,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=156,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.78166667 0.74416667 0.79833333 0.84916667 0.86392405]
----------------------------------------
Trial 4945
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=11,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=101,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68833333 0.6975 0.7975 0.8 0.90901899]
----------------------------------------
Trial 4946
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=133, random_state=100))])
cv score: [0.69166667 0.62333333 0.73916667 0.78916667 0.82278481]
----------------------------------------
Trial 4947
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=90,
random_state=100))])
cv score: [0.67916667 0.59666667 0.715 0.80666667 0.81566456]
----------------------------------------
Trial 4948
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=159,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67666667 0.63583333 0.775 0.75666667 0.83544304]
----------------------------------------
Trial 4949
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.75))])
cv score: [0.595 0.6275 0.59583333 0.61083333 0.67484177]
----------------------------------------
Trial 4950
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=194, random_state=100,
subsample=0.85))])
cv score: [0.6475 0.55 0.6375 0.71833333 0.65743671]
----------------------------------------
Trial 4951
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=44, random_state=100))])
cv score: [0.66916667 0.62166667 0.68708333 0.80125 0.82832278]
----------------------------------------
Trial 4952
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=108,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65416667 0.66 0.73166667 0.80833333 0.81329114]
----------------------------------------
Trial 4953
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='log2',
n_estimators=66, random_state=100,
subsample=0.6))])
cv score: [0.66916667 0.63583333 0.73583333 0.81 0.73971519]
----------------------------------------
Trial 4954
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.07,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=165,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.65, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68333333 0.68083333 0.79083333 0.80833333 0.85601266]
----------------------------------------
Trial 4955
----------------------------------------
Parameters {'gb__n_estimators': 55, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=55, random_state=100,
subsample=0.95))])
cv score: [0.7575 0.64916667 0.76583333 0.82916667 0.81012658]
----------------------------------------
Trial 4956
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=173, random_state=100,
subsample=0.95))])
cv score: [0.52666667 0.54916667 0.6875 0.7075 0.74129747]
----------------------------------------
Trial 4957
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=27, random_state=100))])
cv score: [0.68333333 0.58833333 0.7475 0.77166667 0.83227848]
----------------------------------------
Trial 4958
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='sqrt',
n_estimators=137, random_state=100,
subsample=0.7))])
cv score: [0.6575 0.66666667 0.675 0.755 0.65268987]
----------------------------------------
Trial 4959
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=64, random_state=100,
subsample=0.9))])
cv score: [0.65416667 0.57083333 0.68083333 0.69666667 0.75237342]
----------------------------------------
Trial 4960
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=71, random_state=100,
subsample=0.65))])
cv score: [0.72416667 0.69 0.77583333 0.83166667 0.79825949]
----------------------------------------
Trial 4961
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=51, random_state=100,
subsample=0.8))])
cv score: [0.69416667 0.66875 0.81333333 0.83083333 0.79746835]
----------------------------------------
Trial 4962
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=191,
random_state=100))])
cv score: [0.76458333 0.49375 0.78791667 0.70291667 0.79232595]
----------------------------------------
Trial 4963
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=83, random_state=100))])
cv score: [0.6525 0.68166667 0.7475 0.795 0.84177215]
----------------------------------------
Trial 4964
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='log2',
n_estimators=69, random_state=100,
subsample=0.75))])
cv score: [0.61041667 0.68 0.71583333 0.84166667 0.79509494]
----------------------------------------
Trial 4965
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=47,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=1.0, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.675 0.6325 0.76166667 0.74333333 0.82990506]
----------------------------------------
Trial 4966
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.95))])
cv score: [0.6325 0.565 0.67833333 0.74916667 0.75474684]
----------------------------------------
Trial 4967
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.07, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=98,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.68166667 0.655 0.74333333 0.78083333 0.79984177]
----------------------------------------
Trial 4968
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=65,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.75625 0.73875 0.77125 0.85625 0.83860759]
----------------------------------------
Trial 4969
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=144,
random_state=100))])
cv score: [0.66833333 0.58583333 0.7175 0.81416667 0.83148734]
----------------------------------------
Trial 4970
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.9, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.001, max_delta_step=None,
max_depth=6, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=66,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65333333 0.71375 0.76208333 0.85916667 0.87420886]
----------------------------------------
Trial 4971
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.85))])
cv score: [0.66833333 0.57416667 0.695 0.83 0.85363924]
----------------------------------------
Trial 4972
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=14, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=180,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.85, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.77 0.73416667 0.78041667 0.85166667 0.86708861]
----------------------------------------
Trial 4973
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=88,
random_state=100))])
cv score: [0.67666667 0.60166667 0.7175 0.8075 0.81962025]
----------------------------------------
Trial 4974
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=170, random_state=100))])
cv score: [0.72166667 0.67458333 0.77583333 0.83375 0.82594937]
----------------------------------------
Trial 4975
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=109, random_state=100,
subsample=0.8))])
cv score: [0.62333333 0.62583333 0.70083333 0.83083333 0.8085443 ]
----------------------------------------
Trial 4976
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='log2',
n_estimators=16, random_state=100,
subsample=0.75))])
cv score: [0.61916667 0.61666667 0.79166667 0.78083333 0.84414557]
----------------------------------------
Trial 4977
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=1.0,
max_delta_step=None, max_depth=4,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=96,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.95, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.725 0.66333333 0.7275 0.7825 0.78322785]
----------------------------------------
Trial 4978
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.6, gamma=0.003, gpu_id=None,
importance_type='gain',
interaction_constraints=None,
learning_rate=0.003, max_delta_step=None,
max_depth=5, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=43,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.69 0.75458333 0.785 0.86375 0.85205696]
----------------------------------------
Trial 4979
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=91,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.67583333 0.655 0.78166667 0.81916667 0.89477848]
----------------------------------------
Trial 4980
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=158,
random_state=100,
subsample=0.75))])
cv score: [0.66166667 0.64916667 0.75083333 0.8025 0.82515823]
----------------------------------------
Trial 4981
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='sqrt',
n_estimators=168, random_state=100,
subsample=0.7))])
cv score: [0.6175 0.60166667 0.645 0.74833333 0.76977848]
----------------------------------------
Trial 4982
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=66, random_state=100,
subsample=0.9))])
cv score: [0.67083333 0.62 0.64916667 0.82 0.76107595]
----------------------------------------
Trial 4983
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.65))])
cv score: [0.60333333 0.59833333 0.66833333 0.70416667 0.80933544]
----------------------------------------
Trial 4984
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='sqrt',
n_estimators=144, random_state=100,
subsample=0.6))])
cv score: [0.72416667 0.6575 0.72833333 0.77916667 0.81012658]
----------------------------------------
Trial 4985
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=63, random_state=100))])
cv score: [0.65916667 0.59916667 0.71666667 0.775 0.83860759]
----------------------------------------
Trial 4986
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.8, gamma=0.001, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=136,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.8, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.63166667 0.61416667 0.77333333 0.765 0.83386076]
----------------------------------------
Trial 4987
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='log2',
n_estimators=127, random_state=100,
subsample=0.8))])
cv score: [0.67416667 0.675 0.71583333 0.83583333 0.83623418]
----------------------------------------
Trial 4988
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.7,
max_delta_step=None, max_depth=10,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=71,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.54416667 0.59666667 0.7875 0.74333333 0.74683544]
----------------------------------------
Trial 4989
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.03, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=7,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=11,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.75, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.73791667 0.70541667 0.76 0.84416667 0.89556962]
----------------------------------------
Trial 4990
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=56, random_state=100))])
cv score: [0.7475 0.6825 0.77333333 0.84 0.86155063]
----------------------------------------
Trial 4991
----------------------------------------
Parameters {'xgb__n_estimators': 26, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, gamma=0.01, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.3,
max_delta_step=None, max_depth=12,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=26,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.6, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.65083333 0.69583333 0.8 0.78916667 0.84889241]
----------------------------------------
Trial 4992
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=150, random_state=100))])
cv score: [0.71833333 0.68291667 0.78333333 0.83833333 0.84098101]
----------------------------------------
Trial 4993
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=None, booster=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=1.0, gamma=0.3, gpu_id=None,
importance_type='gain',
interaction_constraints=None, learning_rate=0.1,
max_delta_step=None, max_depth=5,
min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=152,
n_jobs=None, num_parallel_tree=None,
random_state=100, reg_alpha=None,
reg_lambda=None, scale_pos_weight=None,
subsample=0.9, tree_method=None,
validate_parameters=None, verbosity=None))])
cv score: [0.71583333 0.68583333 0.7975 0.76583333 0.86471519]
----------------------------------------
Trial 4994
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=182, random_state=100))])
cv score: [0.69166667 0.62166667 0.73916667 0.8175 0.82238924]
----------------------------------------
Trial 4995
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=118, random_state=100))])
cv score: [0.68625 0.63208333 0.74333333 0.82375 0.81962025]
----------------------------------------
Trial 4996
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=183,
random_state=100))])
cv score: [0.67666667 0.57666667 0.72 0.81833333 0.83939873]
----------------------------------------
Trial 4997
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='sqrt',
n_estimators=101, random_state=100,
subsample=0.85))])
cv score: [0.67083333 0.57833333 0.69916667 0.77416667 0.77689873]
----------------------------------------
Trial 4998
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=18, random_state=100,
subsample=0.9))])
cv score: [0.73083333 0.62666667 0.82416667 0.795 0.71202532]
----------------------------------------
Trial 4999
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=75, random_state=100,
subsample=0.65))])
cv score: [0.64333333 0.57833333 0.72333333 0.81083333 0.85601266]
----------------------------------------
Selecting and refitting best classifier
----------------------------------------
[16:46:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
best score: 0.8209303797468355
best pipe: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=152,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
In [23]:
# best_estimator_drain = XGBClassifier(base_score=0.5, booster='gbtree',
# colsample_bylevel=1, colsample_bynode=1,
# colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
# importance_type='gain',
# interaction_constraints='', learning_rate=0.3,
# max_delta_step=0, max_depth=3,
# min_child_weight=1,
# monotone_constraints='()', n_estimators=100,
# n_jobs=16, num_parallel_tree=1, random_state=100,
# reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
# subsample=0.8, tree_method='exact',
# validate_parameters=1, verbosity=None)
In [17]:
best_estimator_drain.fit(X_drain_rest, y_drain_rest)
[16:46:49] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Out[17]:
Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=152,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
In [18]:
mmh.get_test_scores(X_drain_rest, y_drain_rest, X_drain_test, y_drain_test, best_estimator_drain)
[16:46:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. Performance on the test set Classification accuracy: 0.7983193277310925 AUROC: 0.581578947368421 Recall: 0.9 F1 Score: 0.8823529411764707 Log-loss: 6.965897712956967
In [19]:
cm_drain_test = confusion_matrix(y_drain_test, best_estimator_drain.predict(X_drain_test), normalize='true')
mmh.plot_confusion_matrix(cm_drain_test, [0, 1])
Doubly Robust Learning: include drain as a feature into model_y.¶
- In doubly robust estimators of ATEs and CATES, the treatment variable is included in
model_y
In [20]:
y_full = csdh_doc['recurrence']
X_full = csdh_doc.drop(['recurrence'], axis=1)
In [21]:
# Split into validation set and rest
X_rest, X_test, y_rest, y_test = train_test_split(X_full, y_full,
test_size=0.20,
random_state=random_state,
stratify=y_full)
# Split rest into train and test set
X_train, X_val, y_train, y_val = train_test_split(X_rest, y_rest,
test_size=0.20,
random_state=random_state,
stratify=y_rest)
In [22]:
training_scores, val_scores = mmh.train_and_validate_classifiers(X_train,
y_train,
X_val,
y_val,
names,
classifiers)
[16:46:57] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
In [23]:
mmh.print_metrics_table(training_scores, val_scores, names)
Classification performance on validation set:
----------------Validation----------------- -----------------Training------------------
Method Acc↑ AUROC↑ Recall↑ F1↑ LL↓ Acc↑ AUROC↑ Recall↑ F1↑ LL↓
----------------------------------------------------------------------------------------------------
Dummy 0.916 0.500 0.000 0.000 2.909 0.911 0.500 0.000 0.000 3.090
LR 0.916 0.500 0.000 0.000 2.909 0.911 0.500 0.000 0.000 3.090
Linear SVM 0.916 0.500 0.000 0.000 2.909 0.911 0.500 0.000 0.000 3.090
RBF SVM 0.916 0.500 0.000 0.000 2.909 0.911 0.500 0.000 0.000 3.090
GB 0.916 0.557 0.125 0.200 2.909 0.974 0.853 0.706 0.828 0.909
RF 0.916 0.500 0.000 0.000 2.909 1.000 1.000 1.000 1.000 0.000
XGB 0.905 0.494 0.000 0.000 3.272 1.000 1.000 1.000 1.000 0.000
model_y K-Fold cross validation for hyperparameter tuning and model selection¶
In [24]:
# do the search
_,_, best_estimator = mmh.randomized_search_cv(X_rest, y_rest,
search_space,
cv=cv_5,
refit=True,
score='roc_auc',
n_iter=5000,
verbose=True)
cv strategy StratifiedKFold(n_splits=5, random_state=100, shuffle=True)
----------------------------------------
Trial 0
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=171,
random_state=100))])
cv score: [0.72557471 0.65373563 0.59051724 0.63953488 0.42377261]
----------------------------------------
Trial 1
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=76,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6566092 0.58477011 0.54741379 0.56847545 0.48320413]
----------------------------------------
Trial 2
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=95, random_state=100,
subsample=0.8))])
cv score: [0.58189655 0.74712644 0.59626437 0.68087855 0.42377261]
----------------------------------------
Trial 3
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52729885 0.61637931 0.61637931 0.54651163 0.36046512]
----------------------------------------
Trial 4
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=84,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.66235632 0.67241379 0.65503876 0.39405685]
----------------------------------------
Trial 5
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
n_estimators=183, random_state=100,
subsample=0.6))])
cv score: [0.70258621 0.67241379 0.64942529 0.65503876 0.41602067]
----------------------------------------
Trial 6
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67528736 0.625 0.60344828 0.61498708 0.42764858]
----------------------------------------
Trial 7
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=77, random_state=100))])
cv score: [0.73275862 0.61925287 0.67816092 0.55813953 0.39793282]
----------------------------------------
Trial 8
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, n_estimators=143,
random_state=100))])
cv score: [0.62643678 0.68965517 0.5933908 0.61757106 0.39405685]
----------------------------------------
Trial 9
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=102, random_state=100,
subsample=0.7))])
cv score: [0.60344828 0.45545977 0.5862069 0.6744186 0.49741602]
----------------------------------------
Trial 10
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78663793 0.5941092 0.67744253 0.62403101 0.47416021]
----------------------------------------
Trial 11
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=89,
random_state=100))])
cv score: [0.55172414 0.67528736 0.64008621 0.72739018 0.35465116]
----------------------------------------
Trial 12
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5545977 0.69827586 0.62787356 0.62273902 0.40180879]
----------------------------------------
Trial 13
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=33,
random_state=100))])
cv score: [0.61853448 0.60272989 0.56824713 0.57105943 0.45348837]
----------------------------------------
Trial 14
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=177, random_state=100))])
cv score: [0.72988506 0.63505747 0.65948276 0.59043928 0.40439276]
----------------------------------------
Trial 15
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=71, random_state=100,
subsample=0.65))])
cv score: [0.63362069 0.68678161 0.48994253 0.59560724 0.5245478 ]
----------------------------------------
Trial 16
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='sqrt',
n_estimators=137, random_state=100,
subsample=0.95))])
cv score: [0.58908046 0.72844828 0.57183908 0.63178295 0.41602067]
----------------------------------------
Trial 17
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=72,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80531609 0.5862069 0.63362069 0.56459948 0.3875969 ]
----------------------------------------
Trial 18
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=19,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7191092 0.65804598 0.72198276 0.55943152 0.4005168 ]
----------------------------------------
Trial 19
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=73,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 20
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=17,
random_state=100))])
cv score: [0.64942529 0.58908046 0.58045977 0.66020672 0.47739018]
----------------------------------------
Trial 21
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=57,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.60201149 0.6566092 0.60723514 0.46640827]
----------------------------------------
Trial 22
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=196, random_state=100))])
cv score: [0.63505747 0.65948276 0.61925287 0.66666667 0.40310078]
----------------------------------------
Trial 23
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=91, random_state=100))])
cv score: [0.76293103 0.65517241 0.65804598 0.62919897 0.45219638]
----------------------------------------
Trial 24
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6566092 0.59913793 0.61278736 0.54005168 0.36692506]
----------------------------------------
Trial 25
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=80,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.625 0.67241379 0.62643678 0.58268734 0.45736434]
----------------------------------------
Trial 26
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=61,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.69827586 0.68103448 0.65762274 0.45090439]
----------------------------------------
Trial 27
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=101,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55172414 0.65948276 0.62931034 0.4870801 0.42894057]
----------------------------------------
Trial 28
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='log2',
n_estimators=119, random_state=100,
subsample=0.6))])
cv score: [0.73850575 0.61063218 0.68247126 0.60077519 0.43281654]
----------------------------------------
Trial 29
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=60,
random_state=100))])
cv score: [0.65373563 0.70977011 0.63505747 0.68992248 0.45478036]
----------------------------------------
Trial 30
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=47,
random_state=100))])
cv score: [0.78017241 0.64367816 0.62428161 0.64470284 0.3875969 ]
----------------------------------------
Trial 31
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=20, random_state=100))])
cv score: [0.80603448 0.62787356 0.60632184 0.64664083 0.43346253]
----------------------------------------
Trial 32
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=136, random_state=100))])
cv score: [0.71551724 0.63936782 0.67241379 0.5878553 0.3875969 ]
----------------------------------------
Trial 33
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=158, random_state=100,
subsample=0.75))])
cv score: [0.53448276 0.62931034 0.38793103 0.62273902 0.5 ]
----------------------------------------
Trial 34
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=151,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54741379 0.69252874 0.51867816 0.60206718 0.41085271]
----------------------------------------
Trial 35
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=131,
random_state=100))])
cv score: [0.74425287 0.64798851 0.69971264 0.64341085 0.43927649]
----------------------------------------
Trial 36
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=162,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5704023 0.69252874 0.62643678 0.61369509 0.3875969 ]
----------------------------------------
Trial 37
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=148,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67528736 0.66522989 0.67097701 0.67571059 0.41085271]
----------------------------------------
Trial 38
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='sqrt',
n_estimators=171, random_state=100,
subsample=0.9))])
cv score: [0.56896552 0.68390805 0.60344828 0.66795866 0.42118863]
----------------------------------------
Trial 39
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=10,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77442529 0.64511494 0.62356322 0.52971576 0.47351421]
----------------------------------------
Trial 40
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=136,
random_state=100))])
cv score: [0.61494253 0.67816092 0.60775862 0.71834625 0.40956072]
----------------------------------------
Trial 41
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='log2',
n_estimators=171, random_state=100,
subsample=0.85))])
cv score: [0.59913793 0.52873563 0.50718391 0.58656331 0.50775194]
----------------------------------------
Trial 42
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=83, random_state=100,
subsample=0.95))])
cv score: [0.66522989 0.64798851 0.51149425 0.52196382 0.5 ]
----------------------------------------
Trial 43
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='log2',
n_estimators=129, random_state=100,
subsample=0.65))])
cv score: [0.75431034 0.65517241 0.72557471 0.66149871 0.44056848]
----------------------------------------
Trial 44
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.69971264 0.43821839 0.55943152 0.42118863]
----------------------------------------
Trial 45
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76867816 0.67241379 0.67816092 0.61175711 0.44767442]
----------------------------------------
Trial 46
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=54, random_state=100,
subsample=0.7))])
cv score: [0.60201149 0.63649425 0.53017241 0.69121447 0.52196382]
----------------------------------------
Trial 47
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=57, random_state=100,
subsample=0.6))])
cv score: [0.75574713 0.64439655 0.72701149 0.65180879 0.42764858]
----------------------------------------
Trial 48
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=111, random_state=100,
subsample=0.6))])
cv score: [0.63362069 0.68534483 0.53017241 0.69250646 0.42248062]
----------------------------------------
Trial 49
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=120, random_state=100,
subsample=0.7))])
cv score: [0.71264368 0.68821839 0.61063218 0.6498708 0.49354005]
----------------------------------------
Trial 50
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=11,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70545977 0.62571839 0.63864943 0.56136951 0.40762274]
----------------------------------------
Trial 51
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=156, random_state=100,
subsample=0.65))])
cv score: [0.5545977 0.71982759 0.6091954 0.67183463 0.4250646 ]
----------------------------------------
Trial 52
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
n_estimators=58, random_state=100,
subsample=0.85))])
cv score: [0.63074713 0.75287356 0.62212644 0.73643411 0.43927649]
----------------------------------------
Trial 53
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=71,
random_state=100))])
cv score: [0.61566092 0.59913793 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 54
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=97, random_state=100,
subsample=0.95))])
cv score: [0.66810345 0.70545977 0.60201149 0.69250646 0.41602067]
----------------------------------------
Trial 55
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=123, random_state=100,
subsample=0.7))])
cv score: [0.76867816 0.62068966 0.7341954 0.64728682 0.36821705]
----------------------------------------
Trial 56
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.66954023 0.5704023 0.59302326 0.45994832]
----------------------------------------
Trial 57
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='log2',
n_estimators=75, random_state=100,
subsample=0.8))])
cv score: [0.56752874 0.66666667 0.53735632 0.54392765 0.52067183]
----------------------------------------
Trial 58
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=43, random_state=100,
subsample=0.7))])
cv score: [0.56896552 0.44683908 0.56178161 0.7377261 0.55684755]
----------------------------------------
Trial 59
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=97,
random_state=100, subsample=0.7))])
cv score: [0.62212644 0.70402299 0.56752874 0.63178295 0.42894057]
----------------------------------------
Trial 60
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=58, random_state=100))])
cv score: [0.68390805 0.60775862 0.57327586 0.58527132 0.42248062]
----------------------------------------
Trial 61
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=176, random_state=100,
subsample=0.9))])
cv score: [0.59195402 0.72557471 0.54166667 0.66795866 0.42635659]
----------------------------------------
Trial 62
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=34, random_state=100,
subsample=0.6))])
cv score: [0.36637931 0.45258621 0.57902299 0.66149871 0.5245478 ]
----------------------------------------
Trial 63
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=138, random_state=100,
subsample=0.7))])
cv score: [0.56178161 0.72988506 0.57902299 0.70671835 0.40956072]
----------------------------------------
Trial 64
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=95, random_state=100))])
cv score: [0.75574713 0.65804598 0.65229885 0.64211886 0.44186047]
----------------------------------------
Trial 65
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
n_estimators=115, random_state=100,
subsample=0.75))])
cv score: [0.5316092 0.70977011 0.61206897 0.70671835 0.46640827]
----------------------------------------
Trial 66
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=57,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75143678 0.59626437 0.67241379 0.59043928 0.38307494]
----------------------------------------
Trial 67
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=165, random_state=100,
subsample=0.7))])
cv score: [0.74568966 0.66091954 0.68965517 0.6744186 0.42248062]
----------------------------------------
Trial 68
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=151,
random_state=100))])
cv score: [0.66235632 0.63793103 0.62787356 0.63953488 0.45090439]
----------------------------------------
Trial 69
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.71695402 0.58189655 0.53100775 0.44315245]
----------------------------------------
Trial 70
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=68,
random_state=100))])
cv score: [0.625 0.63649425 0.57758621 0.58397933 0.375323 ]
----------------------------------------
Trial 71
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='sqrt',
n_estimators=58,
random_state=100))])
cv score: [0.6637931 0.69971264 0.70402299 0.63953488 0.43540052]
----------------------------------------
Trial 72
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=169,
random_state=100))])
cv score: [0.65373563 0.64798851 0.62212644 0.65245478 0.45090439]
----------------------------------------
Trial 73
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.72126437 0.60775862 0.65373563 0.60852713 0.41989664]
----------------------------------------
Trial 74
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=62, random_state=100))])
cv score: [0.57686782 0.64295977 0.59267241 0.59302326 0.4496124 ]
----------------------------------------
Trial 75
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55100575 0.59841954 0.47126437 0.5251938 0.39728682]
----------------------------------------
Trial 76
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=159,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.69109195 0.69396552 0.63824289 0.4373385 ]
----------------------------------------
Trial 77
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=187, random_state=100))])
cv score: [0.62356322 0.70402299 0.62787356 0.68604651 0.43540052]
----------------------------------------
Trial 78
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.62787356 0.62787356 0.60594315 0.40180879]
----------------------------------------
Trial 79
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=121,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.70114943 0.63218391 0.62661499 0.45736434]
----------------------------------------
Trial 80
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=52,
random_state=100))])
cv score: [0.58405172 0.63505747 0.6954023 0.68152455 0.39147287]
----------------------------------------
Trial 81
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=65, random_state=100))])
cv score: [0.62931034 0.67385057 0.62356322 0.6744186 0.41731266]
----------------------------------------
Trial 82
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71623563 0.53376437 0.58405172 0.53682171 0.3998708 ]
----------------------------------------
Trial 83
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64367816 0.6637931 0.57758621 0.53617571 0.45865633]
----------------------------------------
Trial 84
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=121, random_state=100,
subsample=0.85))])
cv score: [0.67528736 0.7112069 0.55172414 0.64341085 0.49354005]
----------------------------------------
Trial 85
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=155,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.73132184 0.58764368 0.53488372 0.39147287]
----------------------------------------
Trial 86
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=125,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.68821839 0.57902299 0.57881137 0.40180879]
----------------------------------------
Trial 87
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76867816 0.56321839 0.68103448 0.57428941 0.43863049]
----------------------------------------
Trial 88
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=40, random_state=100,
subsample=0.95))])
cv score: [0.59482759 0.74137931 0.54454023 0.60335917 0.34173127]
----------------------------------------
Trial 89
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=50, random_state=100))])
cv score: [0.56609195 0.63793103 0.63218391 0.71576227 0.32299742]
----------------------------------------
Trial 90
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=14, random_state=100,
subsample=0.95))])
cv score: [0.69683908 0.71408046 0.46264368 0.58139535 0.45219638]
----------------------------------------
Trial 91
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=198, random_state=100,
subsample=0.85))])
cv score: [0.56178161 0.74568966 0.61781609 0.70155039 0.39147287]
----------------------------------------
Trial 92
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=150, random_state=100))])
cv score: [0.68821839 0.6637931 0.65229885 0.6744186 0.44315245]
----------------------------------------
Trial 93
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
n_estimators=15, random_state=100,
subsample=0.8))])
cv score: [0.63074713 0.70905172 0.52658046 0.58914729 0.41860465]
----------------------------------------
Trial 94
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.95))])
cv score: [0.6408046 0.66954023 0.5704023 0.6873385 0.44186047]
----------------------------------------
Trial 95
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=166, random_state=100))])
cv score: [0.71695402 0.63505747 0.65086207 0.5878553 0.41602067]
----------------------------------------
Trial 96
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77586207 0.57327586 0.67600575 0.59819121 0.43152455]
----------------------------------------
Trial 97
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=113,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80675287 0.60272989 0.66954023 0.61692506 0.40891473]
----------------------------------------
Trial 98
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=134,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62068966 0.63649425 0.63074713 0.60335917 0.43023256]
----------------------------------------
Trial 99
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.67097701 0.67528736 0.60594315 0.42118863]
----------------------------------------
Trial 100
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.67816092 0.58333333 0.66149871 0.4250646 ]
----------------------------------------
Trial 101
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='sqrt',
n_estimators=114, random_state=100,
subsample=0.65))])
cv score: [0.71982759 0.68821839 0.58477011 0.70284238 0.48320413]
----------------------------------------
Trial 102
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
n_estimators=158, random_state=100,
subsample=0.95))])
cv score: [0.56609195 0.71264368 0.6637931 0.6498708 0.32041344]
----------------------------------------
Trial 103
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68390805 0.59913793 0.6566092 0.56459948 0.43410853]
----------------------------------------
Trial 104
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=38, random_state=100))])
cv score: [0.5308908 0.60344828 0.65517241 0.62919897 0.43540052]
----------------------------------------
Trial 105
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=179,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.70545977 0.73132184 0.66731266 0.40310078]
----------------------------------------
Trial 106
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=85,
random_state=100))])
cv score: [0.64798851 0.68821839 0.52586207 0.56847545 0.44186047]
----------------------------------------
Trial 107
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64942529 0.73563218 0.5933908 0.61757106 0.43152455]
----------------------------------------
Trial 108
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=150, random_state=100))])
cv score: [0.6408046 0.65373563 0.58045977 0.66408269 0.38630491]
----------------------------------------
Trial 109
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=93, random_state=100,
subsample=0.8))])
cv score: [0.68678161 0.65086207 0.63505747 0.65891473 0.46640827]
----------------------------------------
Trial 110
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='log2',
n_estimators=18, random_state=100,
subsample=0.9))])
cv score: [0.79310345 0.58117816 0.76436782 0.67894057 0.37984496]
----------------------------------------
Trial 111
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=158, random_state=100,
subsample=0.95))])
cv score: [0.58189655 0.65948276 0.45833333 0.61111111 0.45865633]
----------------------------------------
Trial 112
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=146,
random_state=100))])
cv score: [0.63218391 0.63793103 0.44396552 0.61369509 0.44315245]
----------------------------------------
Trial 113
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.72557471 0.55028736 0.54521964 0.42118863]
----------------------------------------
Trial 114
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=108,
random_state=100))])
cv score: [0.64295977 0.68390805 0.54597701 0.56136951 0.43410853]
----------------------------------------
Trial 115
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=51,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77514368 0.58908046 0.64295977 0.56072351 0.38372093]
----------------------------------------
Trial 116
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=164, random_state=100))])
cv score: [0.59770115 0.66522989 0.59482759 0.63953488 0.44186047]
----------------------------------------
Trial 117
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=52, random_state=100))])
cv score: [0.55531609 0.67025862 0.61206897 0.56330749 0.44832041]
----------------------------------------
Trial 118
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='sqrt',
n_estimators=40, random_state=100,
subsample=0.7))])
cv score: [0.60201149 0.6408046 0.62643678 0.58268734 0.40180879]
----------------------------------------
Trial 119
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='log2',
n_estimators=91, random_state=100,
subsample=0.65))])
cv score: [0.5704023 0.66666667 0.42816092 0.66537468 0.41989664]
----------------------------------------
Trial 120
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=63, random_state=100))])
cv score: [0.73563218 0.64224138 0.72557471 0.53359173 0.37209302]
----------------------------------------
Trial 121
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65517241 0.71982759 0.65804598 0.64082687 0.42183463]
----------------------------------------
Trial 122
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=149,
random_state=100))])
cv score: [0.69971264 0.64798851 0.67385057 0.65891473 0.42764858]
----------------------------------------
Trial 123
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=12, random_state=100,
subsample=0.95))])
cv score: [0.65948276 0.55747126 0.42528736 0.70478036 0.49354005]
----------------------------------------
Trial 124
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=132,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72270115 0.69612069 0.68318966 0.625323 0.34883721]
----------------------------------------
Trial 125
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=47, random_state=100))])
cv score: [0.59267241 0.64727011 0.57255747 0.60723514 0.43992248]
----------------------------------------
Trial 126
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=47, random_state=100,
subsample=0.9))])
cv score: [0.55316092 0.66522989 0.5 0.57622739 0.4870801 ]
----------------------------------------
Trial 127
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=84,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.65086207 0.65948276 0.63049096 0.36821705]
----------------------------------------
Trial 128
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=185, random_state=100,
subsample=0.65))])
cv score: [0.66666667 0.71695402 0.53448276 0.56847545 0.48837209]
----------------------------------------
Trial 129
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=19,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72916667 0.61997126 0.61997126 0.66989664 0.47868217]
----------------------------------------
Trial 130
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=149, random_state=100))])
cv score: [0.59626437 0.67241379 0.58908046 0.625323 0.44315245]
----------------------------------------
Trial 131
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.65948276 0.58189655 0.54651163 0.46640827]
----------------------------------------
Trial 132
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, n_estimators=40,
random_state=100, subsample=0.9))])
cv score: [0.68390805 0.69683908 0.67385057 0.66149871 0.39664083]
----------------------------------------
Trial 133
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=75, random_state=100,
subsample=0.95))])
cv score: [0.63074713 0.71408046 0.50287356 0.67183463 0.46511628]
----------------------------------------
Trial 134
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=123,
random_state=100))])
cv score: [0.69971264 0.64367816 0.65517241 0.65116279 0.41472868]
----------------------------------------
Trial 135
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=71,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.70689655 0.52155172 0.55297158 0.39922481]
----------------------------------------
Trial 136
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.61781609 0.57183908 0.5503876 0.44832041]
----------------------------------------
Trial 137
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=47, random_state=100,
subsample=0.8))])
cv score: [0.55603448 0.76724138 0.59770115 0.70542636 0.43152455]
----------------------------------------
Trial 138
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='sqrt',
n_estimators=52, random_state=100,
subsample=0.85))])
cv score: [0.7183908 0.62356322 0.70545977 0.64211886 0.37855297]
----------------------------------------
Trial 139
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=174,
random_state=100))])
cv score: [0.61637931 0.65804598 0.61135057 0.72351421 0.41860465]
----------------------------------------
Trial 140
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=61, random_state=100))])
cv score: [0.55316092 0.6170977 0.59195402 0.53875969 0.42700258]
----------------------------------------
Trial 141
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=184,
random_state=100))])
cv score: [0.66522989 0.64511494 0.61494253 0.65245478 0.4496124 ]
----------------------------------------
Trial 142
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=159,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77658046 0.64511494 0.70761494 0.63242894 0.43152455]
----------------------------------------
Trial 143
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=89, random_state=100))])
cv score: [0.68103448 0.67097701 0.58764368 0.64728682 0.41731266]
----------------------------------------
Trial 144
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=68, random_state=100))])
cv score: [0.59913793 0.62715517 0.55244253 0.60981912 0.43410853]
----------------------------------------
Trial 145
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=34, random_state=100,
subsample=0.85))])
cv score: [0.50143678 0.75 0.56178161 0.65503876 0.45542636]
----------------------------------------
Trial 146
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=194, random_state=100))])
cv score: [0.61063218 0.71408046 0.65948276 0.67700258 0.44056848]
----------------------------------------
Trial 147
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=72,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.65086207 0.70833333 0.6498708 0.4496124 ]
----------------------------------------
Trial 148
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=175, random_state=100))])
cv score: [0.65517241 0.67528736 0.60488506 0.67829457 0.43410853]
----------------------------------------
Trial 149
----------------------------------------
Parameters {'xgb__n_estimators': 171, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=171,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.68390805 0.65517241 0.61111111 0.3875969 ]
----------------------------------------
Trial 150
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='log2',
n_estimators=183, random_state=100,
subsample=0.65))])
cv score: [0.71982759 0.63362069 0.65517241 0.61886305 0.41860465]
----------------------------------------
Trial 151
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=22,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.71551724 0.5545977 0.61111111 0.41343669]
----------------------------------------
Trial 152
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=13,
random_state=100))])
cv score: [0.64152299 0.59841954 0.56178161 0.51744186 0.40116279]
----------------------------------------
Trial 153
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=10, random_state=100,
subsample=0.9))])
cv score: [0.50718391 0.81681034 0.47054598 0.60788114 0.41085271]
----------------------------------------
Trial 154
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=85, random_state=100,
subsample=0.95))])
cv score: [0.67528736 0.65086207 0.56465517 0.7248062 0.45865633]
----------------------------------------
Trial 155
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=69, random_state=100,
subsample=0.85))])
cv score: [0.59770115 0.68103448 0.58548851 0.46770026 0.49224806]
----------------------------------------
Trial 156
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='sqrt',
n_estimators=132,
random_state=100))])
cv score: [0.58477011 0.70689655 0.50287356 0.56718346 0.44832041]
----------------------------------------
Trial 157
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=71,
random_state=100))])
cv score: [0.57183908 0.59482759 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 158
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=15,
random_state=100))])
cv score: [0.70977011 0.65948276 0.4612069 0.61563307 0.36692506]
----------------------------------------
Trial 159
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=131, random_state=100,
subsample=0.7))])
cv score: [0.57327586 0.68821839 0.57902299 0.65891473 0.47674419]
----------------------------------------
Trial 160
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=110,
random_state=100))])
cv score: [0.74712644 0.64655172 0.70545977 0.64857881 0.43927649]
----------------------------------------
Trial 161
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=175,
random_state=100))])
cv score: [0.65948276 0.64367816 0.61350575 0.65245478 0.44573643]
----------------------------------------
Trial 162
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=61, random_state=100))])
cv score: [0.72413793 0.65517241 0.66954023 0.58268734 0.37984496]
----------------------------------------
Trial 163
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='log2',
n_estimators=106, random_state=100,
subsample=0.9))])
cv score: [0.70977011 0.65373563 0.5316092 0.65762274 0.56589147]
----------------------------------------
Trial 164
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=189, random_state=100))])
cv score: [0.59913793 0.73491379 0.64511494 0.67829457 0.44186047]
----------------------------------------
Trial 165
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=184, random_state=100))])
cv score: [0.73563218 0.64655172 0.65229885 0.64857881 0.41343669]
----------------------------------------
Trial 166
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=179,
random_state=100))])
cv score: [0.75431034 0.64798851 0.69971264 0.64599483 0.44573643]
----------------------------------------
Trial 167
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=155, random_state=100,
subsample=0.65))])
cv score: [0.62931034 0.6795977 0.61494253 0.73255814 0.4870801 ]
----------------------------------------
Trial 168
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=34,
random_state=100))])
cv score: [0.6795977 0.6170977 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 169
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=35, random_state=100,
subsample=0.6))])
cv score: [0.59770115 0.68821839 0.56034483 0.67958656 0.42377261]
----------------------------------------
Trial 170
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54741379 0.7183908 0.58045977 0.51421189 0.44832041]
----------------------------------------
Trial 171
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=178, random_state=100,
subsample=0.95))])
cv score: [0.55172414 0.71408046 0.56609195 0.73643411 0.40439276]
----------------------------------------
Trial 172
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=19,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78017241 0.58261494 0.67241379 0.60400517 0.37403101]
----------------------------------------
Trial 173
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=59,
random_state=100))])
cv score: [0.70977011 0.63649425 0.66522989 0.60981912 0.36950904]
----------------------------------------
Trial 174
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=191, random_state=100))])
cv score: [0.62068966 0.67816092 0.58477011 0.625323 0.43669251]
----------------------------------------
Trial 175
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=140,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.7112069 0.74856322 0.64470284 0.39405685]
----------------------------------------
Trial 176
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=43,
random_state=100))])
cv score: [0.68318966 0.57686782 0.47413793 0.68217054 0.4121447 ]
----------------------------------------
Trial 177
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='sqrt',
n_estimators=14, random_state=100,
subsample=0.65))])
cv score: [0.60775862 0.58333333 0.43103448 0.52842377 0.54780362]
----------------------------------------
Trial 178
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=67, random_state=100))])
cv score: [0.69109195 0.6637931 0.62931034 0.52583979 0.36434109]
----------------------------------------
Trial 179
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
n_estimators=187, random_state=100,
subsample=0.9))])
cv score: [0.63936782 0.69971264 0.61637931 0.54651163 0.47286822]
----------------------------------------
Trial 180
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='log2',
n_estimators=48, random_state=100,
subsample=0.75))])
cv score: [0.52442529 0.54741379 0.42528736 0.49224806 0.35788114]
----------------------------------------
Trial 181
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=199,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77801724 0.59123563 0.7183908 0.60206718 0.4250646 ]
----------------------------------------
Trial 182
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=71,
random_state=100))])
cv score: [0.79741379 0.62068966 0.66163793 0.63178295 0.38630491]
----------------------------------------
Trial 183
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=195,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.73850575 0.61925287 0.70413437 0.47674419]
----------------------------------------
Trial 184
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='log2',
n_estimators=106, random_state=100,
subsample=0.7))])
cv score: [0.64942529 0.66810345 0.49568966 0.65891473 0.4379845 ]
----------------------------------------
Trial 185
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='log2',
n_estimators=22, random_state=100,
subsample=0.65))])
cv score: [0.82471264 0.5933908 0.7112069 0.60723514 0.43152455]
----------------------------------------
Trial 186
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, n_estimators=17,
random_state=100, subsample=0.7))])
cv score: [0.60201149 0.5704023 0.61925287 0.64599483 0.44832041]
----------------------------------------
Trial 187
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=79,
random_state=100))])
cv score: [0.6566092 0.62787356 0.63074713 0.62790698 0.41860465]
----------------------------------------
Trial 188
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=140,
random_state=100))])
cv score: [0.61206897 0.67672414 0.61135057 0.73255814 0.40956072]
----------------------------------------
Trial 189
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=109,
random_state=100))])
cv score: [0.55747126 0.66738506 0.61637931 0.7377261 0.38501292]
----------------------------------------
Trial 190
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=89, random_state=100))])
cv score: [0.58405172 0.70545977 0.64655172 0.6744186 0.42764858]
----------------------------------------
Trial 191
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=117,
random_state=100))])
cv score: [0.68031609 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 192
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=83,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.82758621 0.6091954 0.64798851 0.60723514 0.47609819]
----------------------------------------
Trial 193
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=65, random_state=100))])
cv score: [0.54813218 0.61422414 0.59626437 0.56589147 0.42118863]
----------------------------------------
Trial 194
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70402299 0.7341954 0.5933908 0.62790698 0.4121447 ]
----------------------------------------
Trial 195
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=104,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.69252874 0.5862069 0.54392765 0.43281654]
----------------------------------------
Trial 196
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=72,
random_state=100))])
cv score: [0.63721264 0.59770115 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 197
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52873563 0.7658046 0.66235632 0.57235142 0.45219638]
----------------------------------------
Trial 198
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=168,
random_state=100,
subsample=0.65))])
cv score: [0.62931034 0.7183908 0.56752874 0.60465116 0.48320413]
----------------------------------------
Trial 199
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=98, random_state=100,
subsample=0.95))])
cv score: [0.56896552 0.69109195 0.57758621 0.6873385 0.43410853]
----------------------------------------
Trial 200
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=40,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72557471 0.56896552 0.75574713 0.60788114 0.46705426]
----------------------------------------
Trial 201
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=146, random_state=100,
subsample=0.9))])
cv score: [0.56465517 0.70258621 0.61350575 0.71317829 0.34625323]
----------------------------------------
Trial 202
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=81, random_state=100,
subsample=0.8))])
cv score: [0.58908046 0.71264368 0.41091954 0.58914729 0.55684755]
----------------------------------------
Trial 203
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
n_estimators=155, random_state=100,
subsample=0.65))])
cv score: [0.56896552 0.71695402 0.54166667 0.68087855 0.44444444]
----------------------------------------
Trial 204
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=65, random_state=100,
subsample=0.65))])
cv score: [0.37068966 0.61135057 0.71264368 0.55684755 0.67183463]
----------------------------------------
Trial 205
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=23, random_state=100,
subsample=0.7))])
cv score: [0.625 0.61925287 0.56609195 0.70801034 0.38242894]
----------------------------------------
Trial 206
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
n_estimators=149, random_state=100,
subsample=0.8))])
cv score: [0.61781609 0.74281609 0.59195402 0.69896641 0.40439276]
----------------------------------------
Trial 207
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=191,
random_state=100))])
cv score: [0.54885057 0.65517241 0.58764368 0.73643411 0.40310078]
----------------------------------------
Trial 208
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=56,
random_state=100))])
cv score: [0.63649425 0.60272989 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 209
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='log2',
n_estimators=16, random_state=100,
subsample=0.6))])
cv score: [0.7112069 0.7341954 0.5933908 0.58010336 0.45219638]
----------------------------------------
Trial 210
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=117, random_state=100,
subsample=0.7))])
cv score: [0.5862069 0.61063218 0.62068966 0.74031008 0.53229974]
----------------------------------------
Trial 211
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=194, random_state=100))])
cv score: [0.66810345 0.63505747 0.59770115 0.6124031 0.40180879]
----------------------------------------
Trial 212
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.62068966 0.67097701 0.58397933 0.43927649]
----------------------------------------
Trial 213
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=118, random_state=100))])
cv score: [0.64224138 0.64655172 0.6091954 0.6744186 0.38888889]
----------------------------------------
Trial 214
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='sqrt',
n_estimators=170, random_state=100,
subsample=0.9))])
cv score: [0.56178161 0.65804598 0.55172414 0.61111111 0.47932817]
----------------------------------------
Trial 215
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=87,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69252874 0.60632184 0.61637931 0.61757106 0.40439276]
----------------------------------------
Trial 216
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
n_estimators=191, random_state=100,
subsample=0.7))])
cv score: [0.71695402 0.65086207 0.63793103 0.64082687 0.41989664]
----------------------------------------
Trial 217
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=191, random_state=100))])
cv score: [0.64655172 0.66810345 0.60057471 0.62144703 0.46124031]
----------------------------------------
Trial 218
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=49, random_state=100))])
cv score: [0.55531609 0.67600575 0.61278736 0.56976744 0.43863049]
----------------------------------------
Trial 219
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=177, random_state=100))])
cv score: [0.6566092 0.67528736 0.60201149 0.68217054 0.4379845 ]
----------------------------------------
Trial 220
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=60,
random_state=100,
subsample=0.85))])
cv score: [0.6408046 0.70258621 0.61637931 0.73901809 0.45736434]
----------------------------------------
Trial 221
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.72844828 0.66522989 0.51162791 0.39276486]
----------------------------------------
Trial 222
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=76, random_state=100))])
cv score: [0.76724138 0.625 0.65229885 0.59366925 0.41795866]
----------------------------------------
Trial 223
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=89, random_state=100,
subsample=0.8))])
cv score: [0.6954023 0.62931034 0.68678161 0.65116279 0.43669251]
----------------------------------------
Trial 224
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=138,
random_state=100))])
cv score: [0.68031609 0.57399425 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 225
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=103, random_state=100,
subsample=0.9))])
cv score: [0.57902299 0.72413793 0.50287356 0.70284238 0.5245478 ]
----------------------------------------
Trial 226
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=174, random_state=100))])
cv score: [0.73132184 0.63505747 0.65517241 0.59560724 0.40310078]
----------------------------------------
Trial 227
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=165, random_state=100,
subsample=0.6))])
cv score: [0.58477011 0.67672414 0.47701149 0.56459948 0.4496124 ]
----------------------------------------
Trial 228
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=165, random_state=100,
subsample=0.95))])
cv score: [0.57327586 0.66954023 0.49281609 0.59302326 0.49354005]
----------------------------------------
Trial 229
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=186,
random_state=100))])
cv score: [0.58979885 0.60201149 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 230
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=101, random_state=100,
subsample=0.65))])
cv score: [0.59051724 0.64224138 0.5704023 0.62661499 0.46899225]
----------------------------------------
Trial 231
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=129, random_state=100))])
cv score: [0.74281609 0.64224138 0.66091954 0.5878553 0.39922481]
----------------------------------------
Trial 232
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=196,
random_state=100))])
cv score: [0.62212644 0.65373563 0.60057471 0.6744186 0.4496124 ]
----------------------------------------
Trial 233
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=104, random_state=100))])
cv score: [0.74137931 0.61637931 0.61350575 0.60465116 0.43152455]
----------------------------------------
Trial 234
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=152,
random_state=100))])
cv score: [0.77298851 0.63793103 0.68534483 0.63953488 0.4005168 ]
----------------------------------------
Trial 235
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=10, random_state=100))])
cv score: [0.49568966 0.60847701 0.62715517 0.58462532 0.48514212]
----------------------------------------
Trial 236
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=23,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69037356 0.59841954 0.64008621 0.71963824 0.45671835]
----------------------------------------
Trial 237
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=103,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79454023 0.66954023 0.71336207 0.65826873 0.41472868]
----------------------------------------
Trial 238
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=20, random_state=100))])
cv score: [0.65301724 0.63146552 0.54022989 0.63953488 0.36757106]
----------------------------------------
Trial 239
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=108, random_state=100,
subsample=0.7))])
cv score: [0.57327586 0.49856322 0.59770115 0.66020672 0.45090439]
----------------------------------------
Trial 240
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=130, random_state=100,
subsample=0.75))])
cv score: [0.62643678 0.44683908 0.57902299 0.43281654 0.52583979]
----------------------------------------
Trial 241
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=72,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.63218391 0.58908046 0.59302326 0.41731266]
----------------------------------------
Trial 242
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=28,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 243
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65373563 0.66666667 0.73275862 0.6744186 0.41731266]
----------------------------------------
Trial 244
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=161, random_state=100,
subsample=0.85))])
cv score: [0.63362069 0.6566092 0.52442529 0.68604651 0.40310078]
----------------------------------------
Trial 245
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=183, random_state=100))])
cv score: [0.65373563 0.63649425 0.59482759 0.60465116 0.40956072]
----------------------------------------
Trial 246
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=114, random_state=100))])
cv score: [0.76293103 0.6408046 0.64798851 0.63049096 0.39534884]
----------------------------------------
Trial 247
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=59,
random_state=100))])
cv score: [0.7183908 0.6795977 0.71551724 0.6130491 0.40568475]
----------------------------------------
Trial 248
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=126,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56465517 0.66954023 0.60775862 0.6124031 0.39147287]
----------------------------------------
Trial 249
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=144, random_state=100))])
cv score: [0.56106322 0.68247126 0.59985632 0.5994832 0.4502584 ]
----------------------------------------
Trial 250
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.56465517 0.62931034 0.64211886 0.39793282]
----------------------------------------
Trial 251
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=117,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63362069 0.70545977 0.62356322 0.61111111 0.40310078]
----------------------------------------
Trial 252
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=131,
random_state=100))])
cv score: [0.56321839 0.61063218 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 253
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=74, random_state=100,
subsample=0.75))])
cv score: [0.54741379 0.63074713 0.60201149 0.64728682 0.41860465]
----------------------------------------
Trial 254
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=106,
random_state=100))])
cv score: [0.62643678 0.65229885 0.60488506 0.65633075 0.44573643]
----------------------------------------
Trial 255
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=164,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56752874 0.66235632 0.61494253 0.58010336 0.38113695]
----------------------------------------
Trial 256
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=141, random_state=100))])
cv score: [0.72557471 0.63793103 0.67528736 0.58268734 0.41602067]
----------------------------------------
Trial 257
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=156, random_state=100))])
cv score: [0.63362069 0.65804598 0.58189655 0.67958656 0.39405685]
----------------------------------------
Trial 258
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=131,
random_state=100))])
cv score: [0.77155172 0.63649425 0.6716954 0.63436693 0.4005168 ]
----------------------------------------
Trial 259
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=32, random_state=100))])
cv score: [0.67528736 0.66738506 0.56896552 0.65116279 0.45155039]
----------------------------------------
Trial 260
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.74137931 0.62212644 0.60335917 0.48191214]
----------------------------------------
Trial 261
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='log2',
n_estimators=24, random_state=100,
subsample=0.8))])
cv score: [0.81752874 0.59698276 0.67385057 0.66408269 0.44056848]
----------------------------------------
Trial 262
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.48994253 0.6637931 0.49425287 0.46382429 0.42764858]
----------------------------------------
Trial 263
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=67,
random_state=100))])
cv score: [0.61494253 0.6795977 0.63505747 0.60723514 0.36950904]
----------------------------------------
Trial 264
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=167,
random_state=100))])
cv score: [0.5862069 0.69396552 0.53017241 0.76098191 0.35142119]
----------------------------------------
Trial 265
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.69683908 0.57902299 0.63307494 0.41860465]
----------------------------------------
Trial 266
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=79,
random_state=100))])
cv score: [0.62643678 0.67816092 0.62356322 0.6498708 0.39922481]
----------------------------------------
Trial 267
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=62,
random_state=100))])
cv score: [0.63936782 0.63793103 0.56752874 0.57622739 0.36111111]
----------------------------------------
Trial 268
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=18, random_state=100,
subsample=0.7))])
cv score: [0.61063218 0.48275862 0.56034483 0.67829457 0.42635659]
----------------------------------------
Trial 269
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=103,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73275862 0.65086207 0.69612069 0.60529716 0.43281654]
----------------------------------------
Trial 270
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=142, random_state=100))])
cv score: [0.57902299 0.65086207 0.5933908 0.64211886 0.44056848]
----------------------------------------
Trial 271
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=15,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71336207 0.56752874 0.45977011 0.54651163 0.42377261]
----------------------------------------
Trial 272
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=102, random_state=100))])
cv score: [0.7112069 0.63936782 0.66522989 0.56459948 0.37984496]
----------------------------------------
Trial 273
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=133, random_state=100))])
cv score: [0.55316092 0.68893678 0.60344828 0.62209302 0.4379845 ]
----------------------------------------
Trial 274
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=179, random_state=100,
subsample=0.75))])
cv score: [0.57614943 0.66954023 0.62212644 0.66149871 0.41860465]
----------------------------------------
Trial 275
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.60344828 0.57183908 0.67312661 0.39405685]
----------------------------------------
Trial 276
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55603448 0.63936782 0.58908046 0.53875969 0.45478036]
----------------------------------------
Trial 277
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=129, random_state=100,
subsample=0.85))])
cv score: [0.55747126 0.5704023 0.47988506 0.64341085 0.44186047]
----------------------------------------
Trial 278
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
n_estimators=11, random_state=100,
subsample=0.8))])
cv score: [0.57758621 0.77586207 0.5316092 0.66537468 0.40956072]
----------------------------------------
Trial 279
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=109,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.70258621 0.60775862 0.52971576 0.44056848]
----------------------------------------
Trial 280
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='sqrt',
n_estimators=133, random_state=100,
subsample=0.85))])
cv score: [0.62356322 0.69827586 0.44396552 0.73901809 0.55426357]
----------------------------------------
Trial 281
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=120,
random_state=100))])
cv score: [0.59770115 0.67241379 0.63649425 0.65374677 0.43281654]
----------------------------------------
Trial 282
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75287356 0.63362069 0.71264368 0.67312661 0.4121447 ]
----------------------------------------
Trial 283
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='log2',
n_estimators=110, random_state=100,
subsample=0.95))])
cv score: [0.76436782 0.61494253 0.7420977 0.63178295 0.3630491 ]
----------------------------------------
Trial 284
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(n_estimators=17, random_state=100,
subsample=0.85))])
cv score: [0.64367816 0.67528736 0.57686782 0.63178295 0.40245478]
----------------------------------------
Trial 285
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=155,
random_state=100))])
cv score: [0.65517241 0.63649425 0.63505747 0.63824289 0.4496124 ]
----------------------------------------
Trial 286
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=29, random_state=100))])
cv score: [0.60775862 0.70258621 0.56896552 0.59625323 0.52390181]
----------------------------------------
Trial 287
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=45, random_state=100,
subsample=0.85))])
cv score: [0.59051724 0.66666667 0.52729885 0.70284238 0.47028424]
----------------------------------------
Trial 288
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=108, random_state=100))])
cv score: [0.61494253 0.67672414 0.6091954 0.62015504 0.43669251]
----------------------------------------
Trial 289
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=89,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.67097701 0.64655172 0.57364341 0.43281654]
----------------------------------------
Trial 290
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=150,
random_state=100))])
cv score: [0.72413793 0.64655172 0.58045977 0.62661499 0.42248062]
----------------------------------------
Trial 291
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.85))])
cv score: [0.64367816 0.64367816 0.62643678 0.61111111 0.42377261]
----------------------------------------
Trial 292
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=121,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80028736 0.64224138 0.63721264 0.5878553 0.46059432]
----------------------------------------
Trial 293
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=145, random_state=100))])
cv score: [0.72126437 0.6408046 0.67241379 0.57881137 0.40956072]
----------------------------------------
Trial 294
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=192, random_state=100))])
cv score: [0.73850575 0.65517241 0.64367816 0.64599483 0.41343669]
----------------------------------------
Trial 295
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=163, random_state=100))])
cv score: [0.65373563 0.67816092 0.60201149 0.69379845 0.43604651]
----------------------------------------
Trial 296
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=137, random_state=100,
subsample=0.7))])
cv score: [0.42097701 0.43390805 0.52658046 0.67248062 0.54392765]
----------------------------------------
Trial 297
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=176,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68390805 0.64367816 0.68678161 0.64211886 0.43927649]
----------------------------------------
Trial 298
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=137,
random_state=100))])
cv score: [0.58979885 0.60272989 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 299
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=26,
random_state=100))])
cv score: [0.76436782 0.63936782 0.63146552 0.64599483 0.44379845]
----------------------------------------
Trial 300
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=73, random_state=100,
subsample=0.65))])
cv score: [0.44899425 0.48275862 0.38362069 0.53488372 0.54521964]
----------------------------------------
Trial 301
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55028736 0.69252874 0.57614943 0.54521964 0.4496124 ]
----------------------------------------
Trial 302
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76795977 0.61494253 0.69683908 0.62080103 0.42571059]
----------------------------------------
Trial 303
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75431034 0.63936782 0.56178161 0.62919897 0.39018088]
----------------------------------------
Trial 304
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=40, random_state=100))])
cv score: [0.73706897 0.6329023 0.59626437 0.60723514 0.41020672]
----------------------------------------
Trial 305
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=139,
random_state=100))])
cv score: [0.625 0.63218391 0.5862069 0.63307494 0.45607235]
----------------------------------------
Trial 306
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=199, random_state=100,
subsample=0.75))])
cv score: [0.63936782 0.68103448 0.59482759 0.6873385 0.43410853]
----------------------------------------
Trial 307
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=160, random_state=100))])
cv score: [0.56393678 0.67887931 0.60344828 0.59819121 0.44573643]
----------------------------------------
Trial 308
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=193,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78735632 0.58189655 0.6954023 0.61821705 0.43927649]
----------------------------------------
Trial 309
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=163, random_state=100))])
cv score: [0.56537356 0.67528736 0.58117816 0.61111111 0.44121447]
----------------------------------------
Trial 310
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79022989 0.6091954 0.6566092 0.63049096 0.45671835]
----------------------------------------
Trial 311
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=189, random_state=100))])
cv score: [0.63649425 0.66091954 0.61063218 0.66925065 0.4005168 ]
----------------------------------------
Trial 312
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=108,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77873563 0.6091954 0.71695402 0.55167959 0.34754522]
----------------------------------------
Trial 313
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=127, random_state=100))])
cv score: [0.61350575 0.72772989 0.65445402 0.68475452 0.41602067]
----------------------------------------
Trial 314
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=71, random_state=100,
subsample=0.85))])
cv score: [0.66810345 0.61925287 0.53017241 0.67958656 0.48966408]
----------------------------------------
Trial 315
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=48, random_state=100,
subsample=0.6))])
cv score: [0.59051724 0.6566092 0.50143678 0.58914729 0.48062016]
----------------------------------------
Trial 316
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=122,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.71408046 0.63074713 0.54651163 0.41602067]
----------------------------------------
Trial 317
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=140, random_state=100,
subsample=0.65))])
cv score: [0.31896552 0.69109195 0.5862069 0.65762274 0.49483204]
----------------------------------------
Trial 318
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=79, random_state=100,
subsample=0.9))])
cv score: [0.53304598 0.70402299 0.49425287 0.6369509 0.49095607]
----------------------------------------
Trial 319
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=136,
random_state=100))])
cv score: [0.73132184 0.64655172 0.58764368 0.63565891 0.41860465]
----------------------------------------
Trial 320
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=156,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56465517 0.71551724 0.55747126 0.58527132 0.40826873]
----------------------------------------
Trial 321
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=133, random_state=100,
subsample=0.75))])
cv score: [0.5387931 0.41954023 0.4137931 0.47803618 0.52325581]
----------------------------------------
Trial 322
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='sqrt',
n_estimators=163, random_state=100,
subsample=0.85))])
cv score: [0.64511494 0.67816092 0.43390805 0.64728682 0.46770026]
----------------------------------------
Trial 323
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=24,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68175287 0.56824713 0.71408046 0.59689922 0.44379845]
----------------------------------------
Trial 324
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=164,
random_state=100))])
cv score: [0.61063218 0.67097701 0.58189655 0.60981912 0.48062016]
----------------------------------------
Trial 325
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.8))])
cv score: [0.55603448 0.59770115 0.60344828 0.56459948 0.48966408]
----------------------------------------
Trial 326
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.65229885 0.63649425 0.59689922 0.43540052]
----------------------------------------
Trial 327
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=151, random_state=100))])
cv score: [0.7341954 0.65086207 0.66091954 0.65503876 0.43152455]
----------------------------------------
Trial 328
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=163,
random_state=100))])
cv score: [0.72270115 0.64942529 0.58764368 0.63953488 0.42248062]
----------------------------------------
Trial 329
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=80,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.69252874 0.68821839 0.64728682 0.39664083]
----------------------------------------
Trial 330
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=43,
random_state=100))])
cv score: [0.58764368 0.6487069 0.66020115 0.66472868 0.41537468]
----------------------------------------
Trial 331
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=18,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7737069 0.58333333 0.59985632 0.52583979 0.4754522 ]
----------------------------------------
Trial 332
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=170,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79525862 0.57471264 0.70186782 0.63242894 0.4250646 ]
----------------------------------------
Trial 333
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=96, random_state=100))])
cv score: [0.75143678 0.63505747 0.6566092 0.625323 0.40310078]
----------------------------------------
Trial 334
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=121,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.66954023 0.62212644 0.62015504 0.42635659]
----------------------------------------
Trial 335
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.71982759 0.65948276 0.64599483 0.37080103]
----------------------------------------
Trial 336
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='log2',
n_estimators=58,
random_state=100))])
cv score: [0.77873563 0.64798851 0.73635057 0.67312661 0.41020672]
----------------------------------------
Trial 337
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=57, random_state=100,
subsample=0.65))])
cv score: [0.68534483 0.66522989 0.62212644 0.65503876 0.41472868]
----------------------------------------
Trial 338
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=19,
random_state=100))])
cv score: [0.57255747 0.60201149 0.56106322 0.56782946 0.46640827]
----------------------------------------
Trial 339
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='log2',
n_estimators=57, random_state=100,
subsample=0.75))])
cv score: [0.1954023 0.32327586 0.50431034 0.44379845 0.50258398]
----------------------------------------
Trial 340
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=39,
random_state=100))])
cv score: [0.59626437 0.66163793 0.6566092 0.66149871 0.42764858]
----------------------------------------
Trial 341
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71982759 0.58979885 0.62859195 0.51744186 0.39470284]
----------------------------------------
Trial 342
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=188,
random_state=100, subsample=0.8))])
cv score: [0.67816092 0.70402299 0.64511494 0.66149871 0.4625323 ]
----------------------------------------
Trial 343
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=126,
random_state=100))])
cv score: [0.77586207 0.63362069 0.66451149 0.63049096 0.38888889]
----------------------------------------
Trial 344
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=117, random_state=100,
subsample=0.6))])
cv score: [0.52442529 0.63505747 0.34051724 0.54328165 0.47674419]
----------------------------------------
Trial 345
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=76,
random_state=100))])
cv score: [0.71551724 0.63936782 0.66235632 0.65503876 0.38630491]
----------------------------------------
Trial 346
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=114,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74568966 0.63505747 0.67887931 0.64018088 0.43410853]
----------------------------------------
Trial 347
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.9))])
cv score: [0.78017241 0.60775862 0.72701149 0.64470284 0.36369509]
----------------------------------------
Trial 348
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=149, random_state=100))])
cv score: [0.73563218 0.64224138 0.66954023 0.63565891 0.40826873]
----------------------------------------
Trial 349
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7808908 0.58692529 0.69181034 0.5994832 0.42312661]
----------------------------------------
Trial 350
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=39, random_state=100))])
cv score: [0.7341954 0.63577586 0.59626437 0.60465116 0.41795866]
----------------------------------------
Trial 351
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.68103448 0.54454023 0.65245478 0.41602067]
----------------------------------------
Trial 352
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=90, random_state=100))])
cv score: [0.7816092 0.63002874 0.53376437 0.58462532 0.38242894]
----------------------------------------
Trial 353
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.95))])
cv score: [0.6091954 0.67672414 0.56609195 0.68475452 0.48062016]
----------------------------------------
Trial 354
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=18,
random_state=100))])
cv score: [0.61997126 0.60201149 0.55675287 0.51744186 0.46963824]
----------------------------------------
Trial 355
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='sqrt',
n_estimators=70, random_state=100,
subsample=0.75))])
cv score: [0.62643678 0.44827586 0.57902299 0.43281654 0.52583979]
----------------------------------------
Trial 356
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=13,
random_state=100))])
cv score: [0.58477011 0.60560345 0.51364943 0.52260982 0.46899225]
----------------------------------------
Trial 357
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=80, random_state=100))])
cv score: [0.57686782 0.71551724 0.65373563 0.66602067 0.42894057]
----------------------------------------
Trial 358
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57614943 0.72844828 0.6408046 0.58139535 0.4121447 ]
----------------------------------------
Trial 359
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.75))])
cv score: [0.63649425 0.68247126 0.59913793 0.69509044 0.43410853]
----------------------------------------
Trial 360
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
n_estimators=151, random_state=100,
subsample=0.9))])
cv score: [0.54022989 0.75 0.5862069 0.65762274 0.36821705]
----------------------------------------
Trial 361
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=23,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54310345 0.61494253 0.57758621 0.53359173 0.56330749]
----------------------------------------
Trial 362
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=61,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66954023 0.70114943 0.58477011 0.5878553 0.40310078]
----------------------------------------
Trial 363
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='log2',
n_estimators=102, random_state=100,
subsample=0.85))])
cv score: [0.67672414 0.66666667 0.61206897 0.64599483 0.49741602]
----------------------------------------
Trial 364
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
n_estimators=112, random_state=100,
subsample=0.75))])
cv score: [0.58764368 0.70545977 0.58764368 0.66020672 0.44573643]
----------------------------------------
Trial 365
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
random_state=100))])
cv score: [0.66666667 0.63074713 0.5704023 0.58656331 0.42248062]
----------------------------------------
Trial 366
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5933908 0.71695402 0.61206897 0.63565891 0.40697674]
----------------------------------------
Trial 367
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74425287 0.65373563 0.7191092 0.56912145 0.38888889]
----------------------------------------
Trial 368
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=158, random_state=100))])
cv score: [0.64367816 0.63793103 0.61637931 0.60594315 0.39922481]
----------------------------------------
Trial 369
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68965517 0.58908046 0.6637931 0.63953488 0.38113695]
----------------------------------------
Trial 370
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=17, random_state=100))])
cv score: [0.52514368 0.66307471 0.46336207 0.57299742 0.58268734]
----------------------------------------
Trial 371
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=197,
random_state=100))])
cv score: [0.60488506 0.68821839 0.59051724 0.67571059 0.43023256]
----------------------------------------
Trial 372
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=101, random_state=100,
subsample=0.65))])
cv score: [0.74425287 0.66666667 0.68534483 0.66795866 0.5 ]
----------------------------------------
Trial 373
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=146,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78376437 0.63146552 0.67600575 0.62661499 0.44315245]
----------------------------------------
Trial 374
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=52, random_state=100))])
cv score: [0.55244253 0.65445402 0.58477011 0.58979328 0.47351421]
----------------------------------------
Trial 375
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=160, random_state=100))])
cv score: [0.63793103 0.65229885 0.63218391 0.68604651 0.39922481]
----------------------------------------
Trial 376
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=61,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.65229885 0.59913793 0.61111111 0.43281654]
----------------------------------------
Trial 377
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=44, random_state=100))])
cv score: [0.58908046 0.63074713 0.60057471 0.56976744 0.48449612]
----------------------------------------
Trial 378
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=94, random_state=100))])
cv score: [0.62931034 0.6637931 0.50143678 0.65245478 0.37080103]
----------------------------------------
Trial 379
----------------------------------------
Parameters {'gb__n_estimators': 177, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=177, random_state=100,
subsample=0.85))])
cv score: [0.69971264 0.66235632 0.68534483 0.6369509 0.39534884]
----------------------------------------
Trial 380
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=136,
random_state=100))])
cv score: [0.59626437 0.67600575 0.6408046 0.71705426 0.39534884]
----------------------------------------
Trial 381
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=178, random_state=100,
subsample=0.9))])
cv score: [0.59051724 0.73850575 0.61637931 0.60206718 0.33850129]
----------------------------------------
Trial 382
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=102, random_state=100,
subsample=0.65))])
cv score: [0.59770115 0.70545977 0.49856322 0.60206718 0.4754522 ]
----------------------------------------
Trial 383
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=12,
random_state=100))])
cv score: [0.68031609 0.57399425 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 384
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=53,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.62643678 0.56178161 0.60981912 0.44702842]
----------------------------------------
Trial 385
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78376437 0.67672414 0.6954023 0.61498708 0.45219638]
----------------------------------------
Trial 386
----------------------------------------
Parameters {'rf__n_estimators': 25, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=25,
random_state=100))])
cv score: [0.68031609 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 387
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=163,
random_state=100))])
cv score: [0.77442529 0.63649425 0.68821839 0.64470284 0.39793282]
----------------------------------------
Trial 388
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=119, random_state=100,
subsample=0.65))])
cv score: [0.68103448 0.65229885 0.57183908 0.66537468 0.47157623]
----------------------------------------
Trial 389
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=10, random_state=100))])
cv score: [0.56393678 0.65445402 0.59626437 0.55103359 0.41666667]
----------------------------------------
Trial 390
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=119, random_state=100))])
cv score: [0.61063218 0.7033046 0.65229885 0.67958656 0.42118863]
----------------------------------------
Trial 391
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=33, random_state=100,
subsample=0.6))])
cv score: [0.5862069 0.66091954 0.4841954 0.5878553 0.45736434]
----------------------------------------
Trial 392
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=195,
random_state=100))])
cv score: [0.71408046 0.6637931 0.67241379 0.64470284 0.43669251]
----------------------------------------
Trial 393
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=194,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.69683908 0.61637931 0.62790698 0.42635659]
----------------------------------------
Trial 394
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.73563218 0.60488506 0.53875969 0.38630491]
----------------------------------------
Trial 395
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='sqrt',
n_estimators=160, random_state=100,
subsample=0.9))])
cv score: [0.55747126 0.69396552 0.60344828 0.67571059 0.41602067]
----------------------------------------
Trial 396
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=143, random_state=100))])
cv score: [0.6408046 0.65373563 0.57471264 0.66666667 0.37855297]
----------------------------------------
Trial 397
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=12,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71479885 0.55603448 0.6875 0.56847545 0.47351421]
----------------------------------------
Trial 398
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63218391 0.63649425 0.56752874 0.63824289 0.41602067]
----------------------------------------
Trial 399
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=62, random_state=100,
subsample=0.95))])
cv score: [0.59482759 0.71982759 0.52873563 0.66795866 0.43669251]
----------------------------------------
Trial 400
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=35, random_state=100))])
cv score: [0.62068966 0.62212644 0.71408046 0.59237726 0.40568475]
----------------------------------------
Trial 401
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=71,
random_state=100))])
cv score: [0.59770115 0.64008621 0.60201149 0.69250646 0.37855297]
----------------------------------------
Trial 402
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=126,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.65804598 0.61350575 0.60206718 0.40568475]
----------------------------------------
Trial 403
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=96,
random_state=100))])
cv score: [0.68031609 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 404
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.85))])
cv score: [0.61206897 0.61637931 0.56465517 0.63178295 0.4625323 ]
----------------------------------------
Trial 405
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=160,
random_state=100))])
cv score: [0.54885057 0.66738506 0.58477011 0.74418605 0.40826873]
----------------------------------------
Trial 406
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=106, random_state=100,
subsample=0.75))])
cv score: [0.76005747 0.65948276 0.62212644 0.59819121 0.42248062]
----------------------------------------
Trial 407
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
n_estimators=86, random_state=100,
subsample=0.95))])
cv score: [0.53735632 0.67672414 0.60057471 0.64470284 0.42312661]
----------------------------------------
Trial 408
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=12,
random_state=100))])
cv score: [0.64152299 0.59841954 0.56178161 0.51744186 0.40116279]
----------------------------------------
Trial 409
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=155,
random_state=100))])
cv score: [0.65517241 0.63649425 0.63505747 0.63824289 0.4496124 ]
----------------------------------------
Trial 410
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=22,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.69109195 0.51005747 0.52196382 0.50387597]
----------------------------------------
Trial 411
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=173,
random_state=100))])
cv score: [0.6408046 0.66954023 0.57758621 0.63565891 0.42894057]
----------------------------------------
Trial 412
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=16, random_state=100,
subsample=0.8))])
cv score: [0.55603448 0.45977011 0.51149425 0.65762274 0.66537468]
----------------------------------------
Trial 413
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=180,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56321839 0.7341954 0.58908046 0.625323 0.39147287]
----------------------------------------
Trial 414
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6566092 0.59554598 0.65158046 0.64341085 0.49612403]
----------------------------------------
Trial 415
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56034483 0.70833333 0.74281609 0.66020672 0.3630491 ]
----------------------------------------
Trial 416
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=187, random_state=100))])
cv score: [0.56321839 0.67816092 0.625 0.63307494 0.41602067]
----------------------------------------
Trial 417
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=87,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.61925287 0.66091954 0.58527132 0.374677 ]
----------------------------------------
Trial 418
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=140, random_state=100,
subsample=0.85))])
cv score: [0.70402299 0.64224138 0.65086207 0.66925065 0.40439276]
----------------------------------------
Trial 419
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=172, random_state=100))])
cv score: [0.57183908 0.66954023 0.60775862 0.60206718 0.42635659]
----------------------------------------
Trial 420
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
n_estimators=187, random_state=100,
subsample=0.75))])
cv score: [0.75431034 0.65229885 0.59051724 0.66925065 0.40891473]
----------------------------------------
Trial 421
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=43, random_state=100))])
cv score: [0.75 0.63146552 0.4683908 0.59819121 0.36046512]
----------------------------------------
Trial 422
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=59,
random_state=100))])
cv score: [0.70977011 0.63649425 0.66522989 0.60981912 0.36950904]
----------------------------------------
Trial 423
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.73850575 0.68678161 0.68604651 0.4754522 ]
----------------------------------------
Trial 424
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80675287 0.5783046 0.66451149 0.62596899 0.41795866]
----------------------------------------
Trial 425
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.68103448 0.59051724 0.65503876 0.44832041]
----------------------------------------
Trial 426
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=197, random_state=100,
subsample=0.9))])
cv score: [0.64511494 0.67528736 0.50143678 0.64857881 0.41860465]
----------------------------------------
Trial 427
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.9))])
cv score: [0.61063218 0.68534483 0.51867816 0.69767442 0.46124031]
----------------------------------------
Trial 428
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69971264 0.58045977 0.60272989 0.63307494 0.38953488]
----------------------------------------
Trial 429
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.71408046 0.59482759 0.625323 0.42118863]
----------------------------------------
Trial 430
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='log2',
n_estimators=105, random_state=100,
subsample=0.75))])
cv score: [0.625 0.67672414 0.61494253 0.60335917 0.47157623]
----------------------------------------
Trial 431
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=185, random_state=100,
subsample=0.9))])
cv score: [0.5545977 0.74425287 0.57327586 0.70542636 0.34625323]
----------------------------------------
Trial 432
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80603448 0.5783046 0.69683908 0.61692506 0.4877261 ]
----------------------------------------
Trial 433
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=66,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.6795977 0.5862069 0.60852713 0.42894057]
----------------------------------------
Trial 434
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.63649425 0.63936782 0.62144703 0.36692506]
----------------------------------------
Trial 435
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='log2',
n_estimators=182, random_state=100,
subsample=0.7))])
cv score: [0.5862069 0.68965517 0.56034483 0.67571059 0.45090439]
----------------------------------------
Trial 436
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=172, random_state=100))])
cv score: [0.56178161 0.64942529 0.60847701 0.63178295 0.40180879]
----------------------------------------
Trial 437
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=111,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63793103 0.74568966 0.67816092 0.63436693 0.44315245]
----------------------------------------
Trial 438
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='log2',
n_estimators=104, random_state=100,
subsample=0.7))])
cv score: [0.63362069 0.47988506 0.68821839 0.5878553 0.43669251]
----------------------------------------
Trial 439
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=155, random_state=100,
subsample=0.6))])
cv score: [0.72844828 0.67385057 0.6637931 0.63178295 0.41602067]
----------------------------------------
Trial 440
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=198,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68821839 0.65948276 0.66091954 0.61111111 0.39147287]
----------------------------------------
Trial 441
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=30,
random_state=100))])
cv score: [0.60488506 0.57758621 0.57327586 0.59302326 0.39922481]
----------------------------------------
Trial 442
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=104,
random_state=100))])
cv score: [0.64727011 0.53232759 0.54454023 0.73514212 0.38501292]
----------------------------------------
Trial 443
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=181, random_state=100))])
cv score: [0.58836207 0.67241379 0.58405172 0.62144703 0.42894057]
----------------------------------------
Trial 444
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=179, random_state=100,
subsample=0.9))])
cv score: [0.63218391 0.6566092 0.45114943 0.68604651 0.4250646 ]
----------------------------------------
Trial 445
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=68, random_state=100))])
cv score: [0.57327586 0.70258621 0.64583333 0.67764858 0.42248062]
----------------------------------------
Trial 446
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72126437 0.56609195 0.56465517 0.57364341 0.39018088]
----------------------------------------
Trial 447
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=150, random_state=100,
subsample=0.65))])
cv score: [0.58189655 0.47270115 0.51005747 0.60723514 0.53359173]
----------------------------------------
Trial 448
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6566092 0.68965517 0.62931034 0.64857881 0.4625323 ]
----------------------------------------
Trial 449
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.75143678 0.61781609 0.57622739 0.42248062]
----------------------------------------
Trial 450
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=164,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.72701149 0.67097701 0.5994832 0.40697674]
----------------------------------------
Trial 451
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55172414 0.75143678 0.61494253 0.56459948 0.48320413]
----------------------------------------
Trial 452
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=92, random_state=100,
subsample=0.8))])
cv score: [0.35632184 0.50431034 0.52729885 0.59560724 0.51937984]
----------------------------------------
Trial 453
----------------------------------------
Parameters {'xgb__n_estimators': 95, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=95,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74281609 0.60704023 0.69827586 0.60077519 0.45865633]
----------------------------------------
Trial 454
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73132184 0.59913793 0.50215517 0.5749354 0.45736434]
----------------------------------------
Trial 455
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=27,
random_state=100))])
cv score: [0.6408046 0.70402299 0.61637931 0.59302326 0.29844961]
----------------------------------------
Trial 456
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.67816092 0.57327586 0.63178295 0.40956072]
----------------------------------------
Trial 457
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68678161 0.66522989 0.65804598 0.58527132 0.45478036]
----------------------------------------
Trial 458
----------------------------------------
Parameters {'xgb__n_estimators': 56, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=56,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.67097701 0.50143678 0.61886305 0.43281654]
----------------------------------------
Trial 459
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=176, random_state=100))])
cv score: [0.72270115 0.6408046 0.65373563 0.64599483 0.40826873]
----------------------------------------
Trial 460
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=17,
random_state=100))])
cv score: [0.6091954 0.55603448 0.5308908 0.63307494 0.45607235]
----------------------------------------
Trial 461
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='sqrt',
n_estimators=182, random_state=100,
subsample=0.7))])
cv score: [0.77155172 0.62068966 0.73850575 0.63436693 0.37726098]
----------------------------------------
Trial 462
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=169,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79238506 0.62212644 0.67313218 0.54521964 0.40439276]
----------------------------------------
Trial 463
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=53,
random_state=100))])
cv score: [0.63936782 0.72126437 0.59913793 0.61886305 0.39922481]
----------------------------------------
Trial 464
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=128, random_state=100,
subsample=0.8))])
cv score: [0.61063218 0.64367816 0.51293103 0.69767442 0.44444444]
----------------------------------------
Trial 465
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.55747126 0.47126437 0.52777778 0.44186047]
----------------------------------------
Trial 466
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=31,
random_state=100))])
cv score: [0.6795977 0.6170977 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 467
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=98, random_state=100))])
cv score: [0.66091954 0.61637931 0.57327586 0.58397933 0.42635659]
----------------------------------------
Trial 468
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.68678161 0.6408046 0.5878553 0.45994832]
----------------------------------------
Trial 469
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=97, random_state=100,
subsample=0.8))])
cv score: [0.70258621 0.65086207 0.62931034 0.62790698 0.4496124 ]
----------------------------------------
Trial 470
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=53, random_state=100))])
cv score: [0.62356322 0.62212644 0.58764368 0.55813953 0.47286822]
----------------------------------------
Trial 471
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=160,
random_state=100))])
cv score: [0.61422414 0.6012931 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 472
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=139, random_state=100))])
cv score: [0.60344828 0.67097701 0.58764368 0.63178295 0.45219638]
----------------------------------------
Trial 473
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=179,
random_state=100))])
cv score: [0.77442529 0.62643678 0.68893678 0.64599483 0.40697674]
----------------------------------------
Trial 474
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
n_estimators=125, random_state=100,
subsample=0.65))])
cv score: [0.55603448 0.75287356 0.62931034 0.58656331 0.46382429]
----------------------------------------
Trial 475
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=188, random_state=100))])
cv score: [0.61350575 0.67816092 0.57758621 0.62273902 0.44315245]
----------------------------------------
Trial 476
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53591954 0.73994253 0.66235632 0.60465116 0.34237726]
----------------------------------------
Trial 477
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=86,
random_state=100))])
cv score: [0.5316092 0.68390805 0.61350575 0.7248062 0.49354005]
----------------------------------------
Trial 478
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=154, random_state=100))])
cv score: [0.55962644 0.67744253 0.57686782 0.60658915 0.44767442]
----------------------------------------
Trial 479
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='sqrt',
n_estimators=101, random_state=100,
subsample=0.7))])
cv score: [0.62212644 0.64511494 0.47413793 0.65245478 0.46382429]
----------------------------------------
Trial 480
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=101,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.76436782 0.60488506 0.63436693 0.44056848]
----------------------------------------
Trial 481
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=26, random_state=100))])
cv score: [0.61925287 0.63936782 0.7112069 0.50968992 0.42635659]
----------------------------------------
Trial 482
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=177,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.70402299 0.61781609 0.66795866 0.48191214]
----------------------------------------
Trial 483
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=34, random_state=100))])
cv score: [0.58477011 0.65229885 0.63505747 0.57105943 0.50258398]
----------------------------------------
Trial 484
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=43,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5545977 0.68965517 0.59051724 0.59302326 0.39405685]
----------------------------------------
Trial 485
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=191,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56896552 0.69971264 0.58189655 0.58010336 0.42377261]
----------------------------------------
Trial 486
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=76,
random_state=100))])
cv score: [0.59195402 0.64727011 0.62859195 0.67958656 0.37726098]
----------------------------------------
Trial 487
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='log2',
n_estimators=184, random_state=100,
subsample=0.9))])
cv score: [0.55890805 0.67097701 0.54166667 0.60723514 0.46640827]
----------------------------------------
Trial 488
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=178,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.73275862 0.62931034 0.61498708 0.42894057]
----------------------------------------
Trial 489
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=193,
random_state=100))])
cv score: [0.63505747 0.66810345 0.61925287 0.63565891 0.43152455]
----------------------------------------
Trial 490
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=114,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.72270115 0.57614943 0.47674419 0.36692506]
----------------------------------------
Trial 491
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=98, random_state=100,
subsample=0.85))])
cv score: [0.60632184 0.64798851 0.52873563 0.61369509 0.37338501]
----------------------------------------
Trial 492
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=178,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75 0.66235632 0.70545977 0.62080103 0.43152455]
----------------------------------------
Trial 493
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
n_estimators=64, random_state=100,
subsample=0.75))])
cv score: [0.59051724 0.69827586 0.57758621 0.66666667 0.44444444]
----------------------------------------
Trial 494
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=42,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.49281609 0.64224138 0.67528736 0.58656331 0.44702842]
----------------------------------------
Trial 495
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=72,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.71982759 0.56321839 0.57881137 0.45348837]
----------------------------------------
Trial 496
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=76, random_state=100,
subsample=0.9))])
cv score: [0.61350575 0.61781609 0.52586207 0.74160207 0.46124031]
----------------------------------------
Trial 497
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=19, random_state=100,
subsample=0.95))])
cv score: [0.66091954 0.64224138 0.58908046 0.60465116 0.33462532]
----------------------------------------
Trial 498
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=109,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.6637931 0.62931034 0.63565891 0.40956072]
----------------------------------------
Trial 499
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=36,
random_state=100))])
cv score: [0.70977011 0.63218391 0.63649425 0.61369509 0.38372093]
----------------------------------------
Trial 500
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=104,
random_state=100))])
cv score: [0.71264368 0.64655172 0.66810345 0.65762274 0.40697674]
----------------------------------------
Trial 501
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=87, random_state=100))])
cv score: [0.68390805 0.6795977 0.58764368 0.64470284 0.42635659]
----------------------------------------
Trial 502
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=134, random_state=100))])
cv score: [0.63218391 0.63793103 0.62356322 0.59302326 0.40439276]
----------------------------------------
Trial 503
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.71695402 0.60344828 0.64211886 0.40568475]
----------------------------------------
Trial 504
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=134,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.65086207 0.64798851 0.60465116 0.42377261]
----------------------------------------
Trial 505
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=114,
random_state=100))])
cv score: [0.60344828 0.65948276 0.62787356 0.65633075 0.43023256]
----------------------------------------
Trial 506
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=117, random_state=100,
subsample=0.95))])
cv score: [0.67816092 0.67097701 0.55316092 0.60852713 0.48966408]
----------------------------------------
Trial 507
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=43,
random_state=100))])
cv score: [0.58764368 0.6487069 0.66020115 0.66472868 0.41537468]
----------------------------------------
Trial 508
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=37,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 509
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=37, random_state=100))])
cv score: [0.625 0.62212644 0.71695402 0.58914729 0.40310078]
----------------------------------------
Trial 510
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='log2',
n_estimators=49, random_state=100,
subsample=0.6))])
cv score: [0.64655172 0.63793103 0.56752874 0.64211886 0.51808786]
----------------------------------------
Trial 511
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=32,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.7341954 0.50431034 0.5503876 0.43281654]
----------------------------------------
Trial 512
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=128, random_state=100))])
cv score: [0.56321839 0.68678161 0.60057471 0.62338501 0.43281654]
----------------------------------------
Trial 513
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63218391 0.6408046 0.58045977 0.59431525 0.43152455]
----------------------------------------
Trial 514
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=188, random_state=100))])
cv score: [0.7341954 0.65086207 0.64367816 0.64857881 0.41731266]
----------------------------------------
Trial 515
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=197,
random_state=100,
subsample=0.75))])
cv score: [0.5545977 0.71695402 0.61494253 0.57751938 0.44186047]
----------------------------------------
Trial 516
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=107,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54022989 0.68678161 0.71695402 0.65891473 0.39534884]
----------------------------------------
Trial 517
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=197,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.67672414 0.61063218 0.59431525 0.43281654]
----------------------------------------
Trial 518
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=108, random_state=100))])
cv score: [0.75718391 0.63793103 0.66235632 0.5749354 0.39534884]
----------------------------------------
Trial 519
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77586207 0.58764368 0.68606322 0.57041344 0.38372093]
----------------------------------------
Trial 520
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=186, random_state=100))])
cv score: [0.72126437 0.63793103 0.64224138 0.59431525 0.40568475]
----------------------------------------
Trial 521
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=106,
random_state=100))])
cv score: [0.61206897 0.67456897 0.63721264 0.70801034 0.39470284]
----------------------------------------
Trial 522
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=72, random_state=100))])
cv score: [0.61781609 0.61063218 0.625 0.70542636 0.36563307]
----------------------------------------
Trial 523
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=131,
random_state=100))])
cv score: [0.77011494 0.63074713 0.66163793 0.6369509 0.38888889]
----------------------------------------
Trial 524
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='sqrt',
n_estimators=15, random_state=100,
subsample=0.7))])
cv score: [0.59626437 0.6637931 0.5 0.59819121 0.29328165]
----------------------------------------
Trial 525
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7112069 0.69396552 0.67528736 0.5994832 0.48191214]
----------------------------------------
Trial 526
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=165, random_state=100))])
cv score: [0.6795977 0.66810345 0.63793103 0.6873385 0.45736434]
----------------------------------------
Trial 527
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=197,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68534483 0.65804598 0.65086207 0.62015504 0.43023256]
----------------------------------------
Trial 528
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.6637931 0.59051724 0.57235142 0.43023256]
----------------------------------------
Trial 529
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='log2',
n_estimators=135, random_state=100,
subsample=0.95))])
cv score: [0.49712644 0.77155172 0.4841954 0.39018088 0.4754522 ]
----------------------------------------
Trial 530
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=60,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.70545977 0.72413793 0.64470284 0.42054264]
----------------------------------------
Trial 531
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=139, random_state=100,
subsample=0.65))])
cv score: [0.66522989 0.68821839 0.63649425 0.64728682 0.43281654]
----------------------------------------
Trial 532
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=129, random_state=100))])
cv score: [0.55962644 0.6875 0.59698276 0.62273902 0.43281654]
----------------------------------------
Trial 533
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=192, random_state=100))])
cv score: [0.66091954 0.63505747 0.60201149 0.60852713 0.40568475]
----------------------------------------
Trial 534
----------------------------------------
Parameters {'xgb__n_estimators': 115, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=115,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.69683908 0.50574713 0.55167959 0.42118863]
----------------------------------------
Trial 535
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=101, random_state=100))])
cv score: [0.66235632 0.62931034 0.58333333 0.58268734 0.42248062]
----------------------------------------
Trial 536
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=194, random_state=100))])
cv score: [0.6566092 0.68103448 0.60201149 0.66537468 0.43540052]
----------------------------------------
Trial 537
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=64,
random_state=100))])
cv score: [0.66810345 0.6170977 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 538
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.8))])
cv score: [0.65373563 0.625 0.61494253 0.64211886 0.46124031]
----------------------------------------
Trial 539
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=136,
random_state=100))])
cv score: [0.56321839 0.61063218 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 540
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=94,
random_state=100))])
cv score: [0.59195402 0.67672414 0.49568966 0.66020672 0.38630491]
----------------------------------------
Trial 541
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=66,
random_state=100))])
cv score: [0.57543103 0.65948276 0.67097701 0.7118863 0.37661499]
----------------------------------------
Trial 542
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=106, random_state=100))])
cv score: [0.54741379 0.67025862 0.60847701 0.58139535 0.40503876]
----------------------------------------
Trial 543
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=131, random_state=100))])
cv score: [0.61925287 0.70689655 0.66954023 0.67312661 0.42829457]
----------------------------------------
Trial 544
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=82, random_state=100))])
cv score: [0.58333333 0.68893678 0.64942529 0.66666667 0.44638243]
----------------------------------------
Trial 545
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5933908 0.68390805 0.55747126 0.59689922 0.40956072]
----------------------------------------
Trial 546
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72916667 0.65445402 0.62428161 0.70155039 0.39793282]
----------------------------------------
Trial 547
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=171, random_state=100))])
cv score: [0.72557471 0.63505747 0.65373563 0.59689922 0.40826873]
----------------------------------------
Trial 548
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=112,
random_state=100))])
cv score: [0.62356322 0.6566092 0.59051724 0.64728682 0.47157623]
----------------------------------------
Trial 549
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7579023 0.63146552 0.73706897 0.64018088 0.39147287]
----------------------------------------
Trial 550
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=80,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.56034483 0.61494253 0.63824289 0.41343669]
----------------------------------------
Trial 551
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=130, random_state=100))])
cv score: [0.70545977 0.67528736 0.65086207 0.65633075 0.44315245]
----------------------------------------
Trial 552
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.71264368 0.65373563 0.60723514 0.45607235]
----------------------------------------
Trial 553
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=54,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72988506 0.6637931 0.66235632 0.61950904 0.37144703]
----------------------------------------
Trial 554
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=156, random_state=100,
subsample=0.85))])
cv score: [0.65517241 0.72844828 0.54454023 0.62919897 0.50775194]
----------------------------------------
Trial 555
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=54,
random_state=100))])
cv score: [0.52586207 0.62212644 0.54454023 0.67571059 0.39405685]
----------------------------------------
Trial 556
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=51, random_state=100))])
cv score: [0.6954023 0.67816092 0.60344828 0.62144703 0.40180879]
----------------------------------------
Trial 557
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=189,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79813218 0.58477011 0.68390805 0.59560724 0.42571059]
----------------------------------------
Trial 558
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='log2',
n_estimators=147, random_state=100,
subsample=0.9))])
cv score: [0.63074713 0.66522989 0.50287356 0.63436693 0.43152455]
----------------------------------------
Trial 559
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=36,
random_state=100))])
cv score: [0.76005747 0.63074713 0.64798851 0.69896641 0.42764858]
----------------------------------------
Trial 560
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=86, random_state=100))])
cv score: [0.57758621 0.69181034 0.65373563 0.65762274 0.44444444]
----------------------------------------
Trial 561
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.76867816 0.62931034 0.59819121 0.3875969 ]
----------------------------------------
Trial 562
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=197,
random_state=100))])
cv score: [0.61278736 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 563
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=126,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.70833333 0.57758621 0.58914729 0.39018088]
----------------------------------------
Trial 564
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
n_estimators=149,
random_state=100))])
cv score: [0.58117816 0.63505747 0.64295977 0.6001292 0.42894057]
----------------------------------------
Trial 565
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=32, random_state=100))])
cv score: [0.59554598 0.69324713 0.58333333 0.65891473 0.45348837]
----------------------------------------
Trial 566
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
n_estimators=87, random_state=100,
subsample=0.85))])
cv score: [0.6566092 0.66522989 0.54454023 0.67958656 0.43540052]
----------------------------------------
Trial 567
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.64655172 0.56178161 0.60594315 0.44573643]
----------------------------------------
Trial 568
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=85,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.67672414 0.67097701 0.58139535 0.38888889]
----------------------------------------
Trial 569
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=16, random_state=100,
subsample=0.9))])
cv score: [0.48132184 0.62068966 0.68390805 0.67312661 0.48966408]
----------------------------------------
Trial 570
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=57, random_state=100))])
cv score: [0.74856322 0.66235632 0.64511494 0.6369509 0.48062016]
----------------------------------------
Trial 571
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64511494 0.72413793 0.69683908 0.58656331 0.41731266]
----------------------------------------
Trial 572
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=110,
random_state=100))])
cv score: [0.61637931 0.59841954 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 573
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55747126 0.69252874 0.67241379 0.57622739 0.47028424]
----------------------------------------
Trial 574
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='log2',
n_estimators=131,
random_state=100))])
cv score: [0.62068966 0.68678161 0.62931034 0.67700258 0.43023256]
----------------------------------------
Trial 575
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=15, random_state=100,
subsample=0.8))])
cv score: [0.61063218 0.63649425 0.56034483 0.625323 0.36046512]
----------------------------------------
Trial 576
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.68678161 0.62212644 0.61498708 0.40956072]
----------------------------------------
Trial 577
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, n_estimators=20,
random_state=100,
subsample=0.75))])
cv score: [0.73060345 0.68247126 0.6954023 0.55426357 0.39341085]
----------------------------------------
Trial 578
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65373563 0.64798851 0.64655172 0.60465116 0.41989664]
----------------------------------------
Trial 579
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75431034 0.64942529 0.68821839 0.64211886 0.4496124 ]
----------------------------------------
Trial 580
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=134,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7420977 0.61422414 0.62284483 0.54909561 0.36627907]
----------------------------------------
Trial 581
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.65804598 0.76436782 0.61627907 0.40956072]
----------------------------------------
Trial 582
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=58, random_state=100))])
cv score: [0.7658046 0.62140805 0.62356322 0.60142119 0.42700258]
----------------------------------------
Trial 583
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=22, random_state=100,
subsample=0.95))])
cv score: [0.58764368 0.74568966 0.5316092 0.67958656 0.37596899]
----------------------------------------
Trial 584
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=41,
random_state=100))])
cv score: [0.63793103 0.60632184 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 585
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55603448 0.68965517 0.63649425 0.57364341 0.4496124 ]
----------------------------------------
Trial 586
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=181, random_state=100))])
cv score: [0.61206897 0.66954023 0.58908046 0.62273902 0.44573643]
----------------------------------------
Trial 587
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=51,
random_state=100))])
cv score: [0.56321839 0.60991379 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 588
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=23, random_state=100))])
cv score: [0.54956897 0.59698276 0.45761494 0.5994832 0.5497416 ]
----------------------------------------
Trial 589
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=109,
random_state=100))])
cv score: [0.55747126 0.66738506 0.61637931 0.7377261 0.38501292]
----------------------------------------
Trial 590
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=25, random_state=100,
subsample=0.7))])
cv score: [0.45402299 0.66810345 0.62212644 0.77906977 0.40826873]
----------------------------------------
Trial 591
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=108,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.63936782 0.61206897 0.62661499 0.43152455]
----------------------------------------
Trial 592
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=36,
random_state=100))])
cv score: [0.76005747 0.63074713 0.64798851 0.69896641 0.42764858]
----------------------------------------
Trial 593
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76724138 0.66954023 0.68678161 0.6124031 0.45219638]
----------------------------------------
Trial 594
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=110,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.69396552 0.74712644 0.69379845 0.47157623]
----------------------------------------
Trial 595
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='sqrt',
n_estimators=192,
random_state=100))])
cv score: [0.64224138 0.66666667 0.43390805 0.5994832 0.49354005]
----------------------------------------
Trial 596
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=194,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74497126 0.59123563 0.63649425 0.60658915 0.44186047]
----------------------------------------
Trial 597
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=138, random_state=100))])
cv score: [0.7112069 0.64367816 0.67528736 0.59819121 0.39664083]
----------------------------------------
Trial 598
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=128, random_state=100))])
cv score: [0.60344828 0.70761494 0.6566092 0.68475452 0.41602067]
----------------------------------------
Trial 599
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=164,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55890805 0.58333333 0.62356322 0.60465116 0.41085271]
----------------------------------------
Trial 600
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=141,
random_state=100))])
cv score: [0.66091954 0.64511494 0.65229885 0.64857881 0.4496124 ]
----------------------------------------
Trial 601
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=158, random_state=100,
subsample=0.7))])
cv score: [0.66091954 0.65086207 0.48275862 0.59431525 0.48062016]
----------------------------------------
Trial 602
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='sqrt',
n_estimators=145, random_state=100,
subsample=0.6))])
cv score: [0.5862069 0.55172414 0.45545977 0.54392765 0.45219638]
----------------------------------------
Trial 603
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=31,
random_state=100))])
cv score: [0.61925287 0.60488506 0.55675287 0.51744186 0.46963824]
----------------------------------------
Trial 604
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=194,
random_state=100))])
cv score: [0.7658046 0.63505747 0.69396552 0.63824289 0.39147287]
----------------------------------------
Trial 605
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70186782 0.61278736 0.69827586 0.65245478 0.46705426]
----------------------------------------
Trial 606
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=33, random_state=100))])
cv score: [0.64798851 0.60775862 0.73132184 0.57299742 0.40180879]
----------------------------------------
Trial 607
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71695402 0.57902299 0.62068966 0.69379845 0.40439276]
----------------------------------------
Trial 608
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=40,
random_state=100))])
cv score: [0.68965517 0.62643678 0.68678161 0.69638243 0.38630491]
----------------------------------------
Trial 609
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='sqrt',
n_estimators=101,
random_state=100))])
cv score: [0.68390805 0.7183908 0.48563218 0.56330749 0.53359173]
----------------------------------------
Trial 610
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70258621 0.65229885 0.65373563 0.58527132 0.4625323 ]
----------------------------------------
Trial 611
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=150, random_state=100))])
cv score: [0.71982759 0.6408046 0.66522989 0.56718346 0.4121447 ]
----------------------------------------
Trial 612
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=80,
random_state=100))])
cv score: [0.56609195 0.67385057 0.62715517 0.72351421 0.34366925]
----------------------------------------
Trial 613
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75933908 0.58405172 0.69396552 0.61821705 0.43669251]
----------------------------------------
Trial 614
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=38, random_state=100))])
cv score: [0.64942529 0.62787356 0.44827586 0.65245478 0.41343669]
----------------------------------------
Trial 615
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
n_estimators=23, random_state=100,
subsample=0.7))])
cv score: [0.63505747 0.64224138 0.55028736 0.66408269 0.43346253]
----------------------------------------
Trial 616
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=106,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75 0.65086207 0.66954023 0.60271318 0.44896641]
----------------------------------------
Trial 617
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='sqrt',
n_estimators=37, random_state=100,
subsample=0.9))])
cv score: [0.5933908 0.73706897 0.625 0.67054264 0.48449612]
----------------------------------------
Trial 618
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=137,
random_state=100))])
cv score: [0.77586207 0.64224138 0.68247126 0.64341085 0.39922481]
----------------------------------------
Trial 619
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=196, random_state=100,
subsample=0.95))])
cv score: [0.62787356 0.70689655 0.62643678 0.70930233 0.42248062]
----------------------------------------
Trial 620
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63362069 0.61063218 0.54310345 0.62403101 0.42248062]
----------------------------------------
Trial 621
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55100575 0.58692529 0.47126437 0.52777778 0.3869509 ]
----------------------------------------
Trial 622
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=196, random_state=100))])
cv score: [0.58764368 0.65804598 0.55747126 0.63953488 0.42764858]
----------------------------------------
Trial 623
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=22,
random_state=100))])
cv score: [0.65948276 0.76293103 0.53448276 0.52583979 0.45348837]
----------------------------------------
Trial 624
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='sqrt',
n_estimators=22, random_state=100,
subsample=0.65))])
cv score: [0.54310345 0.62931034 0.72988506 0.64470284 0.50645995]
----------------------------------------
Trial 625
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='log2',
n_estimators=33, random_state=100,
subsample=0.95))])
cv score: [0.53448276 0.64224138 0.52298851 0.67700258 0.44186047]
----------------------------------------
Trial 626
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=108, random_state=100))])
cv score: [0.75718391 0.63793103 0.66235632 0.5749354 0.39534884]
----------------------------------------
Trial 627
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.72844828 0.63793103 0.61498708 0.39664083]
----------------------------------------
Trial 628
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=98, random_state=100))])
cv score: [0.66091954 0.61637931 0.57327586 0.58397933 0.42635659]
----------------------------------------
Trial 629
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='log2',
n_estimators=132, random_state=100,
subsample=0.65))])
cv score: [0.6091954 0.64511494 0.5158046 0.68863049 0.44444444]
----------------------------------------
Trial 630
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
n_estimators=187, random_state=100,
subsample=0.95))])
cv score: [0.63218391 0.67097701 0.57327586 0.67312661 0.43152455]
----------------------------------------
Trial 631
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=112,
random_state=100))])
cv score: [0.68031609 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 632
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=155,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67241379 0.68247126 0.64655172 0.63824289 0.41731266]
----------------------------------------
Trial 633
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=198, random_state=100,
subsample=0.9))])
cv score: [0.66091954 0.65804598 0.58908046 0.64470284 0.40439276]
----------------------------------------
Trial 634
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=184, random_state=100,
subsample=0.85))])
cv score: [0.63649425 0.68678161 0.55028736 0.58656331 0.41602067]
----------------------------------------
Trial 635
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=102,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78376437 0.62356322 0.66666667 0.62144703 0.47222222]
----------------------------------------
Trial 636
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=11,
random_state=100))])
cv score: [0.48204023 0.52155172 0.50933908 0.66989664 0.46576227]
----------------------------------------
Trial 637
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=139,
random_state=100))])
cv score: [0.65948276 0.64511494 0.65517241 0.6498708 0.45219638]
----------------------------------------
Trial 638
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='log2',
n_estimators=113, random_state=100,
subsample=0.95))])
cv score: [0.65948276 0.67528736 0.64655172 0.66666667 0.44444444]
----------------------------------------
Trial 639
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=164,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.63362069 0.67528736 0.68217054 0.42635659]
----------------------------------------
Trial 640
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
n_estimators=71,
random_state=100))])
cv score: [0.46695402 0.67887931 0.58477011 0.76614987 0.3869509 ]
----------------------------------------
Trial 641
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=173, random_state=100))])
cv score: [0.71695402 0.63362069 0.64367816 0.59431525 0.40568475]
----------------------------------------
Trial 642
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=134, random_state=100))])
cv score: [0.56968391 0.68893678 0.61637931 0.60852713 0.44056848]
----------------------------------------
Trial 643
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=141,
random_state=100))])
cv score: [0.625 0.65517241 0.59770115 0.68087855 0.48320413]
----------------------------------------
Trial 644
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=88, random_state=100))])
cv score: [0.5387931 0.66163793 0.59626437 0.58010336 0.3869509 ]
----------------------------------------
Trial 645
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=72,
random_state=100))])
cv score: [0.73275862 0.64655172 0.71264368 0.6498708 0.41989664]
----------------------------------------
Trial 646
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.67241379 0.66522989 0.5878553 0.38372093]
----------------------------------------
Trial 647
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=141, random_state=100))])
cv score: [0.60344828 0.71767241 0.65158046 0.68475452 0.41731266]
----------------------------------------
Trial 648
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67816092 0.67528736 0.63649425 0.61369509 0.40697674]
----------------------------------------
Trial 649
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=29,
random_state=100))])
cv score: [0.66091954 0.56896552 0.51436782 0.52842377 0.49224806]
----------------------------------------
Trial 650
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=117, random_state=100))])
cv score: [0.73275862 0.64224138 0.65517241 0.5749354 0.37596899]
----------------------------------------
Trial 651
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=79, random_state=100,
subsample=0.85))])
cv score: [0.57183908 0.74568966 0.63793103 0.72093023 0.4005168 ]
----------------------------------------
Trial 652
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=19, random_state=100))])
cv score: [0.59482759 0.63649425 0.72270115 0.54069767 0.43927649]
----------------------------------------
Trial 653
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=26,
random_state=100))])
cv score: [0.61206897 0.65301724 0.66594828 0.66925065 0.26485788]
----------------------------------------
Trial 654
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=146, random_state=100))])
cv score: [0.61350575 0.72916667 0.66235632 0.68863049 0.40826873]
----------------------------------------
Trial 655
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=58,
random_state=100))])
cv score: [0.60488506 0.65948276 0.60847701 0.69379845 0.4121447 ]
----------------------------------------
Trial 656
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=37, random_state=100))])
cv score: [0.64511494 0.69037356 0.63218391 0.67377261 0.46834625]
----------------------------------------
Trial 657
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=66,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.6795977 0.67528736 0.65503876 0.39664083]
----------------------------------------
Trial 658
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=11, random_state=100,
subsample=0.85))])
cv score: [0.55028736 0.66666667 0.47413793 0.58656331 0.42764858]
----------------------------------------
Trial 659
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=178,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52729885 0.6954023 0.5933908 0.54392765 0.4250646 ]
----------------------------------------
Trial 660
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
n_estimators=155,
random_state=100))])
cv score: [0.62068966 0.59985632 0.65301724 0.55555556 0.48062016]
----------------------------------------
Trial 661
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=176, random_state=100))])
cv score: [0.67097701 0.67528736 0.6408046 0.67958656 0.45478036]
----------------------------------------
Trial 662
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=126,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.6408046 0.62931034 0.58914729 0.37984496]
----------------------------------------
Trial 663
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='sqrt',
n_estimators=51, random_state=100,
subsample=0.65))])
cv score: [0.55603448 0.61350575 0.63362069 0.62144703 0.45994832]
----------------------------------------
Trial 664
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=198,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74856322 0.63649425 0.69683908 0.64405685 0.45930233]
----------------------------------------
Trial 665
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.60344828 0.64942529 0.60594315 0.39664083]
----------------------------------------
Trial 666
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=51, random_state=100,
subsample=0.65))])
cv score: [0.65948276 0.6566092 0.61494253 0.73901809 0.45994832]
----------------------------------------
Trial 667
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.65))])
cv score: [0.55890805 0.57327586 0.5545977 0.7997416 0.40439276]
----------------------------------------
Trial 668
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='sqrt',
n_estimators=33, random_state=100,
subsample=0.85))])
cv score: [0.54885057 0.40373563 0.50862069 0.62919897 0.51421189]
----------------------------------------
Trial 669
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=97,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72557471 0.55890805 0.54741379 0.57235142 0.39793282]
----------------------------------------
Trial 670
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
n_estimators=174,
random_state=100))])
cv score: [0.63864943 0.59985632 0.56968391 0.51744186 0.40116279]
----------------------------------------
Trial 671
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='log2',
n_estimators=156, random_state=100,
subsample=0.9))])
cv score: [0.61781609 0.67672414 0.5316092 0.60465116 0.40180879]
----------------------------------------
Trial 672
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=10,
random_state=100))])
cv score: [0.63721264 0.59698276 0.56178161 0.52777778 0.40697674]
----------------------------------------
Trial 673
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=38, random_state=100,
subsample=0.85))])
cv score: [0.55316092 0.72270115 0.49712644 0.58656331 0.52842377]
----------------------------------------
Trial 674
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=180,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.69971264 0.64798851 0.5994832 0.43281654]
----------------------------------------
Trial 675
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=76,
random_state=100))])
cv score: [0.71551724 0.63936782 0.66235632 0.65503876 0.38630491]
----------------------------------------
Trial 676
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=70, random_state=100,
subsample=0.9))])
cv score: [0.67241379 0.65086207 0.56896552 0.67829457 0.44186047]
----------------------------------------
Trial 677
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=84,
random_state=100))])
cv score: [0.7816092 0.62643678 0.66594828 0.62919897 0.40826873]
----------------------------------------
Trial 678
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=61,
random_state=100))])
cv score: [0.7112069 0.63793103 0.66235632 0.60594315 0.36950904]
----------------------------------------
Trial 679
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=91, random_state=100,
subsample=0.6))])
cv score: [0.65086207 0.62068966 0.57183908 0.60594315 0.45090439]
----------------------------------------
Trial 680
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=175, random_state=100))])
cv score: [0.72126437 0.64224138 0.65373563 0.64728682 0.40956072]
----------------------------------------
Trial 681
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2',
random_state=100))])
cv score: [0.72557471 0.64798851 0.64367816 0.61498708 0.37984496]
----------------------------------------
Trial 682
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
n_estimators=194, random_state=100,
subsample=0.85))])
cv score: [0.55603448 0.73563218 0.64367816 0.69896641 0.40826873]
----------------------------------------
Trial 683
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=39, random_state=100))])
cv score: [0.7341954 0.63577586 0.59626437 0.60465116 0.41795866]
----------------------------------------
Trial 684
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=39,
random_state=100))])
cv score: [0.57255747 0.60344828 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 685
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69971264 0.61781609 0.70258621 0.64082687 0.42248062]
----------------------------------------
Trial 686
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.64942529 0.48275862 0.57105943 0.47157623]
----------------------------------------
Trial 687
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.6954023 0.62931034 0.62661499 0.41602067]
----------------------------------------
Trial 688
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=63, random_state=100))])
cv score: [0.7112069 0.67241379 0.61494253 0.66020672 0.46899225]
----------------------------------------
Trial 689
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.75))])
cv score: [0.59626437 0.47701149 0.55747126 0.58527132 0.48966408]
----------------------------------------
Trial 690
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='sqrt',
n_estimators=41,
random_state=100))])
cv score: [0.54454023 0.7112069 0.50574713 0.58397933 0.43023256]
----------------------------------------
Trial 691
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=180, random_state=100,
subsample=0.6))])
cv score: [0.40373563 0.52442529 0.5933908 0.75581395 0.43410853]
----------------------------------------
Trial 692
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=55, random_state=100))])
cv score: [0.60632184 0.65373563 0.69252874 0.59043928 0.42764858]
----------------------------------------
Trial 693
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
n_estimators=29, random_state=100,
subsample=0.75))])
cv score: [0.71695402 0.67528736 0.59985632 0.63630491 0.38565891]
----------------------------------------
Trial 694
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72701149 0.6637931 0.72270115 0.65180879 0.41149871]
----------------------------------------
Trial 695
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=144,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.67816092 0.59770115 0.63178295 0.37855297]
----------------------------------------
Trial 696
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, n_estimators=101,
random_state=100, subsample=0.6))])
cv score: [0.63649425 0.69396552 0.52442529 0.7002584 0.4870801 ]
----------------------------------------
Trial 697
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=109, random_state=100))])
cv score: [0.63649425 0.65086207 0.63505747 0.58139535 0.40310078]
----------------------------------------
Trial 698
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56537356 0.66522989 0.7579023 0.61498708 0.3869509 ]
----------------------------------------
Trial 699
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67241379 0.65086207 0.61494253 0.62144703 0.41343669]
----------------------------------------
Trial 700
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74640805 0.59770115 0.47126437 0.5374677 0.36046512]
----------------------------------------
Trial 701
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=49,
random_state=100))])
cv score: [0.52658046 0.62068966 0.5704023 0.65116279 0.39793282]
----------------------------------------
Trial 702
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.58045977 0.54022989 0.71963824 0.44444444]
----------------------------------------
Trial 703
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=184,
random_state=100))])
cv score: [0.625 0.65804598 0.6091954 0.67829457 0.45994832]
----------------------------------------
Trial 704
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=160, random_state=100,
subsample=0.75))])
cv score: [0.5387931 0.54166667 0.51867816 0.39793282 0.59625323]
----------------------------------------
Trial 705
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
n_estimators=168, random_state=100,
subsample=0.7))])
cv score: [0.57183908 0.72126437 0.55316092 0.7002584 0.45478036]
----------------------------------------
Trial 706
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=155, random_state=100,
subsample=0.8))])
cv score: [0.60632184 0.67672414 0.55172414 0.64211886 0.37596899]
----------------------------------------
Trial 707
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='log2',
n_estimators=48, random_state=100,
subsample=0.95))])
cv score: [0.63649425 0.6954023 0.53735632 0.67829457 0.48578811]
----------------------------------------
Trial 708
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63362069 0.61637931 0.68103448 0.55943152 0.46511628]
----------------------------------------
Trial 709
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76221264 0.58764368 0.61997126 0.5620155 0.43540052]
----------------------------------------
Trial 710
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
n_estimators=12, random_state=100,
subsample=0.75))])
cv score: [0.61063218 0.36206897 0.625 0.57364341 0.48449612]
----------------------------------------
Trial 711
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=69,
random_state=100))])
cv score: [0.61566092 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 712
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=79, random_state=100,
subsample=0.9))])
cv score: [0.55747126 0.61350575 0.58045977 0.72997416 0.43152455]
----------------------------------------
Trial 713
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.68103448 0.62212644 0.62403101 0.42118863]
----------------------------------------
Trial 714
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=193,
random_state=100))])
cv score: [0.62068966 0.68103448 0.61637931 0.64470284 0.44444444]
----------------------------------------
Trial 715
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=35,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56321839 0.62356322 0.65373563 0.60335917 0.42764858]
----------------------------------------
Trial 716
----------------------------------------
Parameters {'xgb__n_estimators': 115, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=115,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.65948276 0.56609195 0.62790698 0.40310078]
----------------------------------------
Trial 717
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=18, random_state=100))])
cv score: [0.52873563 0.64798851 0.45402299 0.56847545 0.56589147]
----------------------------------------
Trial 718
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
n_estimators=60, random_state=100,
subsample=0.6))])
cv score: [0.59482759 0.78448276 0.60344828 0.61111111 0.48837209]
----------------------------------------
Trial 719
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=163,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.63649425 0.65804598 0.64211886 0.40180879]
----------------------------------------
Trial 720
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=177, random_state=100))])
cv score: [0.74856322 0.625 0.66666667 0.64728682 0.43152455]
----------------------------------------
Trial 721
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=104, random_state=100))])
cv score: [0.62212644 0.65229885 0.61063218 0.68346253 0.37855297]
----------------------------------------
Trial 722
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=81,
random_state=100))])
cv score: [0.62931034 0.67528736 0.62356322 0.64857881 0.39664083]
----------------------------------------
Trial 723
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=93, random_state=100,
subsample=0.65))])
cv score: [0.57614943 0.68965517 0.54166667 0.63824289 0.45865633]
----------------------------------------
Trial 724
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.71264368 0.62643678 0.59560724 0.39534884]
----------------------------------------
Trial 725
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=83,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53304598 0.67241379 0.48850575 0.60981912 0.42894057]
----------------------------------------
Trial 726
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=84, random_state=100,
subsample=0.65))])
cv score: [0.63936782 0.6566092 0.52729885 0.6369509 0.39922481]
----------------------------------------
Trial 727
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.95))])
cv score: [0.63505747 0.67241379 0.54310345 0.56976744 0.44702842]
----------------------------------------
Trial 728
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=128, random_state=100))])
cv score: [0.75143678 0.63936782 0.6637931 0.64728682 0.41472868]
----------------------------------------
Trial 729
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=144, random_state=100,
subsample=0.85))])
cv score: [0.60344828 0.66522989 0.5158046 0.65503876 0.44702842]
----------------------------------------
Trial 730
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=32,
random_state=100))])
cv score: [0.77729885 0.64798851 0.64295977 0.62144703 0.43087855]
----------------------------------------
Trial 731
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=114, random_state=100))])
cv score: [0.77011494 0.63505747 0.61206897 0.62919897 0.40180879]
----------------------------------------
Trial 732
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=132,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63793103 0.65373563 0.73850575 0.52842377 0.4496124 ]
----------------------------------------
Trial 733
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=127,
random_state=100))])
cv score: [0.61206897 0.68103448 0.61063218 0.71705426 0.42764858]
----------------------------------------
Trial 734
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=134, random_state=100))])
cv score: [0.55316092 0.67241379 0.61494253 0.62144703 0.40116279]
----------------------------------------
Trial 735
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=192,
random_state=100))])
cv score: [0.62787356 0.61063218 0.54022989 0.53100775 0.5749354 ]
----------------------------------------
Trial 736
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=155,
random_state=100))])
cv score: [0.75287356 0.65373563 0.6954023 0.64211886 0.43281654]
----------------------------------------
Trial 737
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76149425 0.64798851 0.68390805 0.68023256 0.47997416]
----------------------------------------
Trial 738
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=101, random_state=100,
subsample=0.95))])
cv score: [0.56896552 0.68821839 0.58189655 0.69121447 0.43281654]
----------------------------------------
Trial 739
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=47,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 740
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=25,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.61350575 0.50574713 0.61111111 0.45994832]
----------------------------------------
Trial 741
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=162,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56321839 0.74712644 0.54741379 0.57235142 0.43023256]
----------------------------------------
Trial 742
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=127,
random_state=100))])
cv score: [0.625 0.66091954 0.60201149 0.66149871 0.49612403]
----------------------------------------
Trial 743
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='sqrt',
n_estimators=78, random_state=100,
subsample=0.85))])
cv score: [0.59195402 0.6408046 0.70402299 0.59819121 0.43669251]
----------------------------------------
Trial 744
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.69827586 0.67241379 0.59560724 0.37726098]
----------------------------------------
Trial 745
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=166,
random_state=100))])
cv score: [0.60344828 0.69324713 0.47988506 0.62596899 0.43087855]
----------------------------------------
Trial 746
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.73563218 0.65086207 0.60206718 0.45607235]
----------------------------------------
Trial 747
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=123, random_state=100,
subsample=0.75))])
cv score: [0.57471264 0.5387931 0.65804598 0.62919897 0.46124031]
----------------------------------------
Trial 748
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=109, random_state=100))])
cv score: [0.75143678 0.64655172 0.64655172 0.62403101 0.4005168 ]
----------------------------------------
Trial 749
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='sqrt',
n_estimators=21, random_state=100,
subsample=0.75))])
cv score: [0.74137931 0.64224138 0.57686782 0.64082687 0.45090439]
----------------------------------------
Trial 750
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=28, random_state=100))])
cv score: [0.63649425 0.68318966 0.49640805 0.69056848 0.4379845 ]
----------------------------------------
Trial 751
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=60,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.68821839 0.56609195 0.61369509 0.38113695]
----------------------------------------
Trial 752
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=60,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.72413793 0.61206897 0.6627907 0.43410853]
----------------------------------------
Trial 753
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.66954023 0.60057471 0.625323 0.45607235]
----------------------------------------
Trial 754
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=199,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 755
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=147, random_state=100,
subsample=0.95))])
cv score: [0.56034483 0.57758621 0.50143678 0.6627907 0.4496124 ]
----------------------------------------
Trial 756
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.73994253 0.61206897 0.61886305 0.44832041]
----------------------------------------
Trial 757
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='sqrt',
n_estimators=174, random_state=100,
subsample=0.6))])
cv score: [0.67241379 0.66091954 0.56609195 0.63307494 0.42764858]
----------------------------------------
Trial 758
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=190,
random_state=100))])
cv score: [0.63649425 0.67097701 0.5862069 0.6369509 0.42894057]
----------------------------------------
Trial 759
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=112, random_state=100))])
cv score: [0.64367816 0.67385057 0.55316092 0.6744186 0.37338501]
----------------------------------------
Trial 760
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.75))])
cv score: [0.69971264 0.66522989 0.55028736 0.64857881 0.49354005]
----------------------------------------
Trial 761
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65301724 0.58189655 0.47126437 0.53617571 0.35400517]
----------------------------------------
Trial 762
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='log2',
n_estimators=44, random_state=100,
subsample=0.75))])
cv score: [0.56178161 0.66810345 0.50143678 0.63178295 0.44056848]
----------------------------------------
Trial 763
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=162,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.67816092 0.63218391 0.51550388 0.39276486]
----------------------------------------
Trial 764
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=176, random_state=100))])
cv score: [0.56609195 0.64942529 0.60991379 0.63565891 0.40245478]
----------------------------------------
Trial 765
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=153, random_state=100,
subsample=0.7))])
cv score: [0.67241379 0.70258621 0.46408046 0.65374677 0.43927649]
----------------------------------------
Trial 766
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=125, random_state=100,
subsample=0.6))])
cv score: [0.62356322 0.65229885 0.51436782 0.65245478 0.48062016]
----------------------------------------
Trial 767
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=33, random_state=100))])
cv score: [0.54741379 0.61206897 0.54885057 0.72609819 0.35400517]
----------------------------------------
Trial 768
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=166,
random_state=100))])
cv score: [0.62068966 0.66522989 0.61494253 0.68346253 0.44573643]
----------------------------------------
Trial 769
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.68678161 0.71695402 0.65439276 0.40180879]
----------------------------------------
Trial 770
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58764368 0.67672414 0.67097701 0.58914729 0.41472868]
----------------------------------------
Trial 771
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=150, random_state=100,
subsample=0.85))])
cv score: [0.62787356 0.66235632 0.45545977 0.57622739 0.47286822]
----------------------------------------
Trial 772
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=102,
random_state=100,
subsample=0.65))])
cv score: [0.63218391 0.71982759 0.57327586 0.625323 0.46124031]
----------------------------------------
Trial 773
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=194, random_state=100,
subsample=0.9))])
cv score: [0.60344828 0.69971264 0.63218391 0.61757106 0.50387597]
----------------------------------------
Trial 774
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=45,
random_state=100))])
cv score: [0.57183908 0.60344828 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 775
----------------------------------------
Parameters {'xgb__n_estimators': 95, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=95,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70474138 0.60704023 0.69971264 0.64728682 0.4618863 ]
----------------------------------------
Trial 776
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=83, random_state=100))])
cv score: [0.54885057 0.6566092 0.60344828 0.5749354 0.38953488]
----------------------------------------
Trial 777
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=133, random_state=100))])
cv score: [0.64367816 0.66666667 0.56752874 0.67312661 0.36046512]
----------------------------------------
Trial 778
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=70, random_state=100))])
cv score: [0.64511494 0.60057471 0.57183908 0.58914729 0.45090439]
----------------------------------------
Trial 779
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=40,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.59770115 0.69971264 0.51421189 0.43152455]
----------------------------------------
Trial 780
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=143,
random_state=100))])
cv score: [0.63649425 0.59770115 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 781
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=105, random_state=100))])
cv score: [0.6091954 0.6875 0.65086207 0.6873385 0.43152455]
----------------------------------------
Trial 782
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=143,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73635057 0.66235632 0.7183908 0.6369509 0.42635659]
----------------------------------------
Trial 783
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=133,
random_state=100))])
cv score: [0.74568966 0.64798851 0.6954023 0.64599483 0.43410853]
----------------------------------------
Trial 784
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=79, random_state=100,
subsample=0.8))])
cv score: [0.71982759 0.65517241 0.70977011 0.65956072 0.4250646 ]
----------------------------------------
Trial 785
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=69,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65373563 0.68821839 0.62212644 0.65503876 0.45607235]
----------------------------------------
Trial 786
----------------------------------------
Parameters {'xgb__n_estimators': 99, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=99,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.7183908 0.69468391 0.61692506 0.39276486]
----------------------------------------
Trial 787
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=127, random_state=100))])
cv score: [0.60057471 0.71982759 0.64511494 0.67635659 0.42377261]
----------------------------------------
Trial 788
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='sqrt',
n_estimators=191, random_state=100,
subsample=0.75))])
cv score: [0.62931034 0.65517241 0.56896552 0.73901809 0.44056848]
----------------------------------------
Trial 789
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=126,
random_state=100))])
cv score: [0.59626437 0.68390805 0.64655172 0.66408269 0.43927649]
----------------------------------------
Trial 790
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=121,
random_state=100))])
cv score: [0.55316092 0.66307471 0.59770115 0.7377261 0.41925065]
----------------------------------------
Trial 791
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74137931 0.65804598 0.68534483 0.64793282 0.43152455]
----------------------------------------
Trial 792
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=129, random_state=100))])
cv score: [0.76436782 0.62931034 0.67097701 0.63824289 0.41602067]
----------------------------------------
Trial 793
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.625 0.67097701 0.59689922 0.4121447 ]
----------------------------------------
Trial 794
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=21, random_state=100,
subsample=0.7))])
cv score: [0.58764368 0.59195402 0.46695402 0.64082687 0.47932817]
----------------------------------------
Trial 795
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=10, random_state=100,
subsample=0.6))])
cv score: [0.83477011 0.63505747 0.77658046 0.66989664 0.45219638]
----------------------------------------
Trial 796
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=65, random_state=100,
subsample=0.85))])
cv score: [0.65229885 0.60344828 0.59482759 0.66666667 0.46770026]
----------------------------------------
Trial 797
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=154,
random_state=100))])
cv score: [0.58908046 0.67816092 0.63936782 0.67958656 0.43410853]
----------------------------------------
Trial 798
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=61, random_state=100))])
cv score: [0.73132184 0.64367816 0.71551724 0.52971576 0.37209302]
----------------------------------------
Trial 799
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.59195402 0.63362069 0.58527132 0.46770026]
----------------------------------------
Trial 800
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=196,
random_state=100))])
cv score: [0.5545977 0.69109195 0.54454023 0.70155039 0.42894057]
----------------------------------------
Trial 801
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=86,
random_state=100))])
cv score: [0.70977011 0.65229885 0.65517241 0.66020672 0.38242894]
----------------------------------------
Trial 802
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=10,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7816092 0.60344828 0.49928161 0.51679587 0.46963824]
----------------------------------------
Trial 803
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=132,
random_state=100,
subsample=0.75))])
cv score: [0.54454023 0.66235632 0.66235632 0.58656331 0.41343669]
----------------------------------------
Trial 804
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
n_estimators=121, random_state=100,
subsample=0.65))])
cv score: [0.6795977 0.66810345 0.61637931 0.66408269 0.44573643]
----------------------------------------
Trial 805
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79382184 0.58908046 0.67600575 0.65116279 0.48514212]
----------------------------------------
Trial 806
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67600575 0.58045977 0.47126437 0.53488372 0.35658915]
----------------------------------------
Trial 807
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.95))])
cv score: [0.62931034 0.70114943 0.66954023 0.64470284 0.54392765]
----------------------------------------
Trial 808
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72413793 0.65948276 0.63936782 0.63178295 0.45865633]
----------------------------------------
Trial 809
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=15,
random_state=100))])
cv score: [0.66810345 0.61997126 0.5158046 0.70994832 0.45930233]
----------------------------------------
Trial 810
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=42,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76939655 0.59770115 0.63218391 0.55620155 0.38565891]
----------------------------------------
Trial 811
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=149, random_state=100,
subsample=0.65))])
cv score: [0.56465517 0.72988506 0.65804598 0.69767442 0.41602067]
----------------------------------------
Trial 812
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.66810345 0.49568966 0.56330749 0.41989664]
----------------------------------------
Trial 813
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=116, random_state=100,
subsample=0.95))])
cv score: [0.58045977 0.71695402 0.59051724 0.62661499 0.41731266]
----------------------------------------
Trial 814
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=34,
random_state=100))])
cv score: [0.6408046 0.60560345 0.55747126 0.51744186 0.41860465]
----------------------------------------
Trial 815
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=138, random_state=100))])
cv score: [0.60201149 0.7191092 0.64942529 0.68346253 0.41666667]
----------------------------------------
Trial 816
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=97, random_state=100,
subsample=0.95))])
cv score: [0.59913793 0.76005747 0.58477011 0.5994832 0.39793282]
----------------------------------------
Trial 817
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=150,
random_state=100))])
cv score: [0.62068966 0.66810345 0.60201149 0.68217054 0.47932817]
----------------------------------------
Trial 818
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55747126 0.68534483 0.63218391 0.64341085 0.44573643]
----------------------------------------
Trial 819
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=76, random_state=100,
subsample=0.6))])
cv score: [0.59195402 0.50287356 0.20258621 0.62273902 0.52196382]
----------------------------------------
Trial 820
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=193, random_state=100))])
cv score: [0.61925287 0.67816092 0.58045977 0.62790698 0.43281654]
----------------------------------------
Trial 821
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=186, random_state=100,
subsample=0.8))])
cv score: [0.69827586 0.66091954 0.62787356 0.66537468 0.42764858]
----------------------------------------
Trial 822
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=167, random_state=100))])
cv score: [0.72988506 0.63793103 0.65948276 0.64341085 0.41472868]
----------------------------------------
Trial 823
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=152, random_state=100))])
cv score: [0.63793103 0.63936782 0.62356322 0.59173127 0.39664083]
----------------------------------------
Trial 824
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=87,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7737069 0.57902299 0.68390805 0.55426357 0.37209302]
----------------------------------------
Trial 825
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=178, random_state=100,
subsample=0.85))])
cv score: [0.5158046 0.61063218 0.5466954 0.61627907 0.45994832]
----------------------------------------
Trial 826
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=140,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56465517 0.69683908 0.66666667 0.61886305 0.39534884]
----------------------------------------
Trial 827
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=62, random_state=100))])
cv score: [0.69109195 0.60632184 0.55747126 0.5749354 0.42635659]
----------------------------------------
Trial 828
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69396552 0.71264368 0.70258621 0.67248062 0.41602067]
----------------------------------------
Trial 829
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='sqrt',
n_estimators=150,
random_state=100))])
cv score: [0.75574713 0.64798851 0.73994253 0.63953488 0.41602067]
----------------------------------------
Trial 830
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=108, random_state=100))])
cv score: [0.61637931 0.70977011 0.65229885 0.67054264 0.42118863]
----------------------------------------
Trial 831
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=181, random_state=100))])
cv score: [0.59698276 0.73850575 0.64224138 0.67183463 0.44315245]
----------------------------------------
Trial 832
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=180,
random_state=100))])
cv score: [0.58979885 0.60272989 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 833
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
n_estimators=58, random_state=100,
subsample=0.9))])
cv score: [0.52155172 0.76293103 0.59051724 0.67700258 0.34366925]
----------------------------------------
Trial 834
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
n_estimators=124, random_state=100,
subsample=0.6))])
cv score: [0.64224138 0.65517241 0.53591954 0.68346253 0.4754522 ]
----------------------------------------
Trial 835
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=51, random_state=100,
subsample=0.85))])
cv score: [0.63218391 0.6795977 0.55747126 0.5878553 0.43927649]
----------------------------------------
Trial 836
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72557471 0.66738506 0.69755747 0.61111111 0.37080103]
----------------------------------------
Trial 837
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='sqrt',
n_estimators=89, random_state=100,
subsample=0.95))])
cv score: [0.63218391 0.70689655 0.5387931 0.66537468 0.43152455]
----------------------------------------
Trial 838
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=56, random_state=100,
subsample=0.7))])
cv score: [0.57327586 0.42241379 0.60344828 0.63307494 0.54521964]
----------------------------------------
Trial 839
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73635057 0.5704023 0.64798851 0.63178295 0.43346253]
----------------------------------------
Trial 840
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65373563 0.68678161 0.56034483 0.53617571 0.46899225]
----------------------------------------
Trial 841
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=174,
random_state=100))])
cv score: [0.72557471 0.65229885 0.58908046 0.63824289 0.42764858]
----------------------------------------
Trial 842
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.71551724 0.61063218 0.64857881 0.43669251]
----------------------------------------
Trial 843
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=64, random_state=100,
subsample=0.9))])
cv score: [0.62643678 0.70402299 0.46695402 0.62144703 0.47803618]
----------------------------------------
Trial 844
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=12, random_state=100))])
cv score: [0.54597701 0.6170977 0.5 0.67312661 0.56976744]
----------------------------------------
Trial 845
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=134, random_state=100))])
cv score: [0.56968391 0.68893678 0.61637931 0.60852713 0.44056848]
----------------------------------------
Trial 846
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=55, random_state=100))])
cv score: [0.61206897 0.70258621 0.64798851 0.7002584 0.42312661]
----------------------------------------
Trial 847
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=113,
random_state=100))])
cv score: [0.59626437 0.6795977 0.58908046 0.6744186 0.43087855]
----------------------------------------
Trial 848
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67528736 0.58764368 0.70258621 0.59560724 0.40116279]
----------------------------------------
Trial 849
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=157, random_state=100,
subsample=0.65))])
cv score: [0.63505747 0.64727011 0.37428161 0.59560724 0.48191214]
----------------------------------------
Trial 850
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=122,
random_state=100))])
cv score: [0.59770115 0.67672414 0.63218391 0.66020672 0.43281654]
----------------------------------------
Trial 851
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=75,
random_state=100))])
cv score: [0.62643678 0.67528736 0.62643678 0.62015504 0.37984496]
----------------------------------------
Trial 852
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=65,
random_state=100))])
cv score: [0.73706897 0.64224138 0.6954023 0.64470284 0.40310078]
----------------------------------------
Trial 853
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=145, random_state=100))])
cv score: [0.60775862 0.71695402 0.65517241 0.67958656 0.41860465]
----------------------------------------
Trial 854
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='sqrt',
n_estimators=63, random_state=100,
subsample=0.6))])
cv score: [0.60057471 0.71551724 0.625 0.73901809 0.40310078]
----------------------------------------
Trial 855
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=195,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64942529 0.68821839 0.65948276 0.64341085 0.4496124 ]
----------------------------------------
Trial 856
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=164, random_state=100))])
cv score: [0.55890805 0.64942529 0.58189655 0.64599483 0.42764858]
----------------------------------------
Trial 857
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=183,
random_state=100))])
cv score: [0.59123563 0.59626437 0.55962644 0.6130491 0.42248062]
----------------------------------------
Trial 858
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=177,
random_state=100))])
cv score: [0.60057471 0.68678161 0.56321839 0.68346253 0.43410853]
----------------------------------------
Trial 859
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.72988506 0.63362069 0.66235632 0.63824289 0.40697674]
----------------------------------------
Trial 860
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=181, random_state=100,
subsample=0.65))])
cv score: [0.64942529 0.70689655 0.64942529 0.67312661 0.43023256]
----------------------------------------
Trial 861
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=104,
random_state=100))])
cv score: [0.74425287 0.64224138 0.70402299 0.63565891 0.41860465]
----------------------------------------
Trial 862
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=114,
random_state=100))])
cv score: [0.7816092 0.63649425 0.67385057 0.63178295 0.39664083]
----------------------------------------
Trial 863
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.72270115 0.70905172 0.6375969 0.3869509 ]
----------------------------------------
Trial 864
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='log2',
n_estimators=49, random_state=100,
subsample=0.9))])
cv score: [0.52011494 0.69396552 0.5 0.66408269 0.37726098]
----------------------------------------
Trial 865
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, n_estimators=63,
random_state=100, subsample=0.6))])
cv score: [0.70114943 0.65373563 0.70833333 0.6498708 0.44444444]
----------------------------------------
Trial 866
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=10, random_state=100,
subsample=0.65))])
cv score: [0.71264368 0.66235632 0.44827586 0.61627907 0.43669251]
----------------------------------------
Trial 867
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=62, random_state=100,
subsample=0.8))])
cv score: [0.51867816 0.52586207 0.49712644 0.6369509 0.33850129]
----------------------------------------
Trial 868
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=112, random_state=100,
subsample=0.7))])
cv score: [0.59051724 0.52729885 0.46408046 0.55555556 0.46770026]
----------------------------------------
Trial 869
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72701149 0.58117816 0.62068966 0.55490956 0.39147287]
----------------------------------------
Trial 870
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.5862069 0.47126437 0.52971576 0.41860465]
----------------------------------------
Trial 871
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=159,
random_state=100))])
cv score: [0.64942529 0.66666667 0.57471264 0.63565891 0.43410853]
----------------------------------------
Trial 872
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=102, random_state=100))])
cv score: [0.60057471 0.70761494 0.64511494 0.6627907 0.4250646 ]
----------------------------------------
Trial 873
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=33,
random_state=100))])
cv score: [0.62068966 0.65948276 0.6329023 0.64728682 0.33074935]
----------------------------------------
Trial 874
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=23,
random_state=100))])
cv score: [0.55962644 0.6487069 0.67528736 0.65762274 0.3249354 ]
----------------------------------------
Trial 875
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=164,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 876
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='log2',
n_estimators=75, random_state=100,
subsample=0.75))])
cv score: [0.67385057 0.42385057 0.43390805 0.51550388 0.45348837]
----------------------------------------
Trial 877
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='sqrt',
n_estimators=85, random_state=100,
subsample=0.95))])
cv score: [0.6091954 0.68103448 0.55890805 0.68863049 0.47416021]
----------------------------------------
Trial 878
----------------------------------------
Parameters {'gb__n_estimators': 88, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='sqrt',
n_estimators=88, random_state=100,
subsample=0.9))])
cv score: [0.49712644 0.80747126 0.53017241 0.54005168 0.45994832]
----------------------------------------
Trial 879
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=66,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.67385057 0.67816092 0.64147287 0.38565891]
----------------------------------------
Trial 880
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=15,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73060345 0.56609195 0.7191092 0.55943152 0.4005168 ]
----------------------------------------
Trial 881
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=182, random_state=100))])
cv score: [0.64798851 0.65804598 0.58333333 0.68604651 0.37984496]
----------------------------------------
Trial 882
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='sqrt',
n_estimators=40, random_state=100,
subsample=0.8))])
cv score: [0.55028736 0.61925287 0.52873563 0.66666667 0.47674419]
----------------------------------------
Trial 883
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=94, random_state=100))])
cv score: [0.60488506 0.68606322 0.65086207 0.65762274 0.43281654]
----------------------------------------
Trial 884
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=12,
random_state=100))])
cv score: [0.58548851 0.60632184 0.51364943 0.52390181 0.46899225]
----------------------------------------
Trial 885
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.72844828 0.68821839 0.58010336 0.40568475]
----------------------------------------
Trial 886
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80244253 0.61206897 0.7183908 0.58656331 0.52067183]
----------------------------------------
Trial 887
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=59, random_state=100,
subsample=0.6))])
cv score: [0.7341954 0.6091954 0.70402299 0.59819121 0.45865633]
----------------------------------------
Trial 888
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=32,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6795977 0.61063218 0.67241379 0.6130491 0.39793282]
----------------------------------------
Trial 889
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=161,
random_state=100))])
cv score: [0.7658046 0.62643678 0.68606322 0.65762274 0.40568475]
----------------------------------------
Trial 890
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77729885 0.56681034 0.68390805 0.54586563 0.374677 ]
----------------------------------------
Trial 891
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=132,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.63793103 0.64367816 0.60206718 0.42377261]
----------------------------------------
Trial 892
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77298851 0.62715517 0.64583333 0.6750646 0.39793282]
----------------------------------------
Trial 893
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=147, random_state=100,
subsample=0.8))])
cv score: [0.66235632 0.65373563 0.62212644 0.67829457 0.49870801]
----------------------------------------
Trial 894
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=14,
random_state=100))])
cv score: [0.54022989 0.64798851 0.61063218 0.50129199 0.54651163]
----------------------------------------
Trial 895
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5704023 0.68103448 0.56034483 0.60077519 0.41602067]
----------------------------------------
Trial 896
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=40,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80100575 0.54813218 0.66235632 0.58010336 0.37015504]
----------------------------------------
Trial 897
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=18,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77298851 0.57471264 0.49066092 0.58397933 0.37403101]
----------------------------------------
Trial 898
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=128, random_state=100,
subsample=0.6))])
cv score: [0.70833333 0.65373563 0.68534483 0.64082687 0.41860465]
----------------------------------------
Trial 899
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79094828 0.64224138 0.68318966 0.62273902 0.46382429]
----------------------------------------
Trial 900
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=47,
random_state=100))])
cv score: [0.66810345 0.6170977 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 901
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=69,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69252874 0.69683908 0.72413793 0.61950904 0.37015504]
----------------------------------------
Trial 902
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=34, random_state=100,
subsample=0.8))])
cv score: [0.54885057 0.60057471 0.56178161 0.56976744 0.42118863]
----------------------------------------
Trial 903
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=186, random_state=100,
subsample=0.6))])
cv score: [0.39798851 0.52873563 0.60057471 0.74806202 0.42894057]
----------------------------------------
Trial 904
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=88,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5387931 0.72988506 0.64511494 0.61886305 0.37596899]
----------------------------------------
Trial 905
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.7))])
cv score: [0.6408046 0.69109195 0.5933908 0.6627907 0.45478036]
----------------------------------------
Trial 906
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=58, random_state=100,
subsample=0.75))])
cv score: [0.59482759 0.65373563 0.5316092 0.59302326 0.42118863]
----------------------------------------
Trial 907
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='sqrt',
n_estimators=125, random_state=100,
subsample=0.6))])
cv score: [0.70545977 0.67097701 0.72270115 0.63953488 0.48449612]
----------------------------------------
Trial 908
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=15, random_state=100))])
cv score: [0.45186782 0.61997126 0.56393678 0.52260982 0.46834625]
----------------------------------------
Trial 909
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=102, random_state=100))])
cv score: [0.64367816 0.64942529 0.62931034 0.57105943 0.40568475]
----------------------------------------
Trial 910
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=81, random_state=100,
subsample=0.95))])
cv score: [0.61494253 0.68678161 0.5316092 0.58010336 0.46511628]
----------------------------------------
Trial 911
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=186, random_state=100))])
cv score: [0.71695402 0.64224138 0.6566092 0.58268734 0.41860465]
----------------------------------------
Trial 912
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=176,
random_state=100))])
cv score: [0.57255747 0.5941092 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 913
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.68390805 0.59482759 0.60981912 0.42635659]
----------------------------------------
Trial 914
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=92,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73132184 0.61135057 0.66451149 0.61821705 0.44186047]
----------------------------------------
Trial 915
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=110, random_state=100,
subsample=0.75))])
cv score: [0.56609195 0.61781609 0.48563218 0.58397933 0.50904393]
----------------------------------------
Trial 916
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.68534483 0.625 0.64341085 0.40180879]
----------------------------------------
Trial 917
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.6))])
cv score: [0.40948276 0.63362069 0.49425287 0.62273902 0.50258398]
----------------------------------------
Trial 918
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=145,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.69396552 0.59195402 0.60077519 0.41343669]
----------------------------------------
Trial 919
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=25, random_state=100,
subsample=0.8))])
cv score: [0.64367816 0.63505747 0.51005747 0.63436693 0.39793282]
----------------------------------------
Trial 920
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=182, random_state=100,
subsample=0.95))])
cv score: [0.52442529 0.70545977 0.60057471 0.67700258 0.41666667]
----------------------------------------
Trial 921
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=58, random_state=100,
subsample=0.65))])
cv score: [0.57327586 0.65804598 0.55316092 0.71059432 0.4625323 ]
----------------------------------------
Trial 922
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=160,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 923
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.73275862 0.6566092 0.62144703 0.39405685]
----------------------------------------
Trial 924
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=188, random_state=100,
subsample=0.95))])
cv score: [0.57758621 0.72701149 0.51867816 0.64470284 0.41602067]
----------------------------------------
Trial 925
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77442529 0.57183908 0.71336207 0.65439276 0.39728682]
----------------------------------------
Trial 926
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=169,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79094828 0.62428161 0.67816092 0.6130491 0.43669251]
----------------------------------------
Trial 927
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=72,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68534483 0.62068966 0.63218391 0.58139535 0.48062016]
----------------------------------------
Trial 928
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=24,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.66666667 0.52155172 0.48449612 0.46640827]
----------------------------------------
Trial 929
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=198,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73132184 0.65086207 0.69971264 0.624677 0.40633075]
----------------------------------------
Trial 930
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=104, random_state=100,
subsample=0.6))])
cv score: [0.63074713 0.63793103 0.54310345 0.625323 0.4005168 ]
----------------------------------------
Trial 931
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
n_estimators=27, random_state=100,
subsample=0.95))])
cv score: [0.64511494 0.6637931 0.53591954 0.62984496 0.41925065]
----------------------------------------
Trial 932
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=81,
random_state=100))])
cv score: [0.58692529 0.66954023 0.65158046 0.69896641 0.37015504]
----------------------------------------
Trial 933
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.62931034 0.60488506 0.60723514 0.33074935]
----------------------------------------
Trial 934
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=12, random_state=100,
subsample=0.85))])
cv score: [0.66954023 0.59626437 0.41954023 0.54392765 0.52713178]
----------------------------------------
Trial 935
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=12, random_state=100,
subsample=0.95))])
cv score: [0.62571839 0.65517241 0.57183908 0.64793282 0.36627907]
----------------------------------------
Trial 936
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='sqrt',
n_estimators=174, random_state=100,
subsample=0.7))])
cv score: [0.60775862 0.65948276 0.58908046 0.67700258 0.45478036]
----------------------------------------
Trial 937
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=45, random_state=100))])
cv score: [0.75718391 0.62859195 0.45402299 0.61111111 0.36821705]
----------------------------------------
Trial 938
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=89, random_state=100))])
cv score: [0.60775862 0.68678161 0.67528736 0.67183463 0.44250646]
----------------------------------------
Trial 939
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=45,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77801724 0.61422414 0.68606322 0.57945736 0.40180879]
----------------------------------------
Trial 940
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=26, random_state=100,
subsample=0.65))])
cv score: [0.625 0.72557471 0.54454023 0.63953488 0.44832041]
----------------------------------------
Trial 941
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=101, random_state=100,
subsample=0.85))])
cv score: [0.60344828 0.75143678 0.56465517 0.62790698 0.50775194]
----------------------------------------
Trial 942
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=127,
random_state=100))])
cv score: [0.57255747 0.59482759 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 943
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=26, random_state=100,
subsample=0.75))])
cv score: [0.5545977 0.64367816 0.48563218 0.34754522 0.40439276]
----------------------------------------
Trial 944
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=137, random_state=100))])
cv score: [0.55603448 0.68390805 0.59770115 0.61821705 0.44186047]
----------------------------------------
Trial 945
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=44, random_state=100,
subsample=0.6))])
cv score: [0.38218391 0.48850575 0.5545977 0.65503876 0.50129199]
----------------------------------------
Trial 946
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=63,
random_state=100))])
cv score: [0.57974138 0.66020115 0.6637931 0.70607235 0.38113695]
----------------------------------------
Trial 947
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=75,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61781609 0.66954023 0.61637931 0.61111111 0.39793282]
----------------------------------------
Trial 948
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=105,
random_state=100))])
cv score: [0.62356322 0.65229885 0.6091954 0.65374677 0.4379845 ]
----------------------------------------
Trial 949
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
n_estimators=184, random_state=100,
subsample=0.6))])
cv score: [0.59482759 0.70977011 0.6091954 0.6873385 0.48837209]
----------------------------------------
Trial 950
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58548851 0.71192529 0.78448276 0.57299742 0.40374677]
----------------------------------------
Trial 951
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=183,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 952
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='sqrt',
n_estimators=65, random_state=100,
subsample=0.9))])
cv score: [0.72413793 0.63793103 0.61637931 0.66666667 0.41602067]
----------------------------------------
Trial 953
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.69971264 0.58189655 0.62273902 0.44702842]
----------------------------------------
Trial 954
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=114, random_state=100,
subsample=0.75))])
cv score: [0.53735632 0.54454023 0.57471264 0.55167959 0.45090439]
----------------------------------------
Trial 955
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.64942529 0.54597701 0.45607235 0.36046512]
----------------------------------------
Trial 956
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=167, random_state=100))])
cv score: [0.56824713 0.67097701 0.60201149 0.60594315 0.43669251]
----------------------------------------
Trial 957
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.6))])
cv score: [0.68821839 0.65517241 0.6091954 0.71059432 0.47157623]
----------------------------------------
Trial 958
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=102, random_state=100))])
cv score: [0.63362069 0.6637931 0.51724138 0.66537468 0.37984496]
----------------------------------------
Trial 959
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=28, random_state=100,
subsample=0.9))])
cv score: [0.67528736 0.64798851 0.4683908 0.65245478 0.42118863]
----------------------------------------
Trial 960
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=122,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52155172 0.66091954 0.63649425 0.58139535 0.45090439]
----------------------------------------
Trial 961
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=198, random_state=100))])
cv score: [0.61781609 0.69683908 0.61925287 0.67958656 0.43023256]
----------------------------------------
Trial 962
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=33,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71982759 0.59195402 0.49928161 0.54521964 0.37403101]
----------------------------------------
Trial 963
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=72,
random_state=100))])
cv score: [0.62212644 0.67385057 0.63218391 0.61757106 0.38242894]
----------------------------------------
Trial 964
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=30,
random_state=100))])
cv score: [0.61637931 0.60272989 0.56824713 0.57041344 0.45348837]
----------------------------------------
Trial 965
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=113,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.6637931 0.64942529 0.62273902 0.41343669]
----------------------------------------
Trial 966
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=156, random_state=100,
subsample=0.9))])
cv score: [0.52873563 0.70977011 0.5545977 0.69767442 0.38113695]
----------------------------------------
Trial 967
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=126, random_state=100))])
cv score: [0.55890805 0.67816092 0.61781609 0.61369509 0.39793282]
----------------------------------------
Trial 968
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=92,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.73275862 0.61637931 0.57235142 0.41731266]
----------------------------------------
Trial 969
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=189,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.69252874 0.61063218 0.60723514 0.42248062]
----------------------------------------
Trial 970
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=175,
random_state=100))])
cv score: [0.60632184 0.68965517 0.56896552 0.68475452 0.43152455]
----------------------------------------
Trial 971
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=155, random_state=100))])
cv score: [0.77442529 0.63362069 0.59626437 0.62919897 0.39082687]
----------------------------------------
Trial 972
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=196,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.68103448 0.61350575 0.62273902 0.39793282]
----------------------------------------
Trial 973
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61781609 0.69252874 0.57471264 0.6744186 0.4496124 ]
----------------------------------------
Trial 974
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=66,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63218391 0.6637931 0.55747126 0.60465116 0.40697674]
----------------------------------------
Trial 975
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74856322 0.67528736 0.67241379 0.61498708 0.45801034]
----------------------------------------
Trial 976
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.8441092 0.56896552 0.64152299 0.53165375 0.46770026]
----------------------------------------
Trial 977
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=143, random_state=100))])
cv score: [0.72413793 0.63649425 0.67385057 0.58268734 0.40956072]
----------------------------------------
Trial 978
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=137, random_state=100))])
cv score: [0.59841954 0.72413793 0.64727011 0.68410853 0.41925065]
----------------------------------------
Trial 979
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=162,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78017241 0.6408046 0.71551724 0.64599483 0.42118863]
----------------------------------------
Trial 980
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=74, random_state=100))])
cv score: [0.60057471 0.64511494 0.64942529 0.59431525 0.41085271]
----------------------------------------
Trial 981
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.57183908 0.57758621 0.6124031 0.42764858]
----------------------------------------
Trial 982
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='sqrt',
n_estimators=91, random_state=100,
subsample=0.6))])
cv score: [0.68103448 0.625 0.49568966 0.65633075 0.41731266]
----------------------------------------
Trial 983
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=48,
random_state=100))])
cv score: [0.75 0.62787356 0.6954023 0.6498708 0.40697674]
----------------------------------------
Trial 984
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
n_estimators=76, random_state=100,
subsample=0.9))])
cv score: [0.55747126 0.68678161 0.59482759 0.73643411 0.42635659]
----------------------------------------
Trial 985
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='sqrt',
n_estimators=12, random_state=100,
subsample=0.9))])
cv score: [0.625 0.7112069 0.6091954 0.5245478 0.36046512]
----------------------------------------
Trial 986
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=29, random_state=100))])
cv score: [0.76436782 0.68678161 0.59195402 0.64599483 0.50710594]
----------------------------------------
Trial 987
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=191,
random_state=100))])
cv score: [0.61925287 0.65229885 0.60488506 0.67958656 0.45607235]
----------------------------------------
Trial 988
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=136, random_state=100,
subsample=0.7))])
cv score: [0.58764368 0.70833333 0.59770115 0.62403101 0.45865633]
----------------------------------------
Trial 989
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=24,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.5933908 0.66954023 0.62403101 0.45930233]
----------------------------------------
Trial 990
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.67816092 0.60201149 0.63049096 0.40310078]
----------------------------------------
Trial 991
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=54,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 992
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.72557471 0.58908046 0.58397933 0.4496124 ]
----------------------------------------
Trial 993
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=103,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53017241 0.73132184 0.55172414 0.59043928 0.44832041]
----------------------------------------
Trial 994
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=39, random_state=100,
subsample=0.95))])
cv score: [0.59626437 0.73275862 0.59626437 0.61498708 0.48578811]
----------------------------------------
Trial 995
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=177, random_state=100))])
cv score: [0.65086207 0.63936782 0.60201149 0.60852713 0.40310078]
----------------------------------------
Trial 996
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=54,
random_state=100))])
cv score: [0.79454023 0.62068966 0.66307471 0.63307494 0.39147287]
----------------------------------------
Trial 997
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='sqrt',
n_estimators=91, random_state=100,
subsample=0.6))])
cv score: [0.58189655 0.68247126 0.44971264 0.63307494 0.45478036]
----------------------------------------
Trial 998
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=37,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.69683908 0.6408046 0.60206718 0.46124031]
----------------------------------------
Trial 999
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=75, random_state=100))])
cv score: [0.64511494 0.59770115 0.57758621 0.59431525 0.43927649]
----------------------------------------
Trial 1000
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67816092 0.6637931 0.65804598 0.67312661 0.4250646 ]
----------------------------------------
Trial 1001
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=65,
random_state=100))])
cv score: [0.59626437 0.66091954 0.61135057 0.70801034 0.41149871]
----------------------------------------
Trial 1002
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=20,
random_state=100))])
cv score: [0.78951149 0.6566092 0.63864943 0.625323 0.43863049]
----------------------------------------
Trial 1003
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=59, random_state=100))])
cv score: [0.56968391 0.64152299 0.58979885 0.59431525 0.43863049]
----------------------------------------
Trial 1004
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=48,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.69683908 0.58764368 0.5878553 0.41731266]
----------------------------------------
Trial 1005
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.6))])
cv score: [0.74712644 0.64224138 0.68390805 0.62919897 0.40568475]
----------------------------------------
Trial 1006
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=59,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69324713 0.60201149 0.68390805 0.54005168 0.37209302]
----------------------------------------
Trial 1007
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
n_estimators=153,
random_state=100))])
cv score: [0.62068966 0.62356322 0.55890805 0.61369509 0.51937984]
----------------------------------------
Trial 1008
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.70545977 0.56896552 0.5749354 0.47674419]
----------------------------------------
Trial 1009
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=49, random_state=100,
subsample=0.65))])
cv score: [0.66666667 0.65229885 0.61637931 0.73643411 0.46640827]
----------------------------------------
Trial 1010
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=160, random_state=100))])
cv score: [0.55890805 0.67528736 0.57614943 0.61498708 0.44509044]
----------------------------------------
Trial 1011
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67816092 0.69683908 0.67241379 0.6369509 0.44315245]
----------------------------------------
Trial 1012
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=93,
random_state=100))])
cv score: [0.57183908 0.59626437 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 1013
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=70, random_state=100))])
cv score: [0.73706897 0.6566092 0.68390805 0.5994832 0.39276486]
----------------------------------------
Trial 1014
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=63, random_state=100))])
cv score: [0.60488506 0.65373563 0.66235632 0.59043928 0.41860465]
----------------------------------------
Trial 1015
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=25, random_state=100,
subsample=0.6))])
cv score: [0.50143678 0.74712644 0.57471264 0.53488372 0.36692506]
----------------------------------------
Trial 1016
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
n_estimators=39, random_state=100,
subsample=0.75))])
cv score: [0.54310345 0.71982759 0.50431034 0.68604651 0.37338501]
----------------------------------------
Trial 1017
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=130,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.6091954 0.58045977 0.55555556 0.42635659]
----------------------------------------
Trial 1018
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=90,
random_state=100))])
cv score: [0.57183908 0.59770115 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 1019
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=75,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 1020
----------------------------------------
Parameters {'gb__n_estimators': 177, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
n_estimators=177, random_state=100,
subsample=0.6))])
cv score: [0.72844828 0.65373563 0.65948276 0.62403101 0.40697674]
----------------------------------------
Trial 1021
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=72,
random_state=100))])
cv score: [0.62356322 0.62787356 0.57183908 0.63436693 0.39405685]
----------------------------------------
Trial 1022
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=126, random_state=100,
subsample=0.9))])
cv score: [0.60488506 0.66522989 0.62212644 0.58268734 0.4754522 ]
----------------------------------------
Trial 1023
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='log2',
n_estimators=118, random_state=100,
subsample=0.6))])
cv score: [0.67385057 0.67097701 0.59051724 0.59043928 0.47932817]
----------------------------------------
Trial 1024
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=197,
random_state=100))])
cv score: [0.6408046 0.67385057 0.62356322 0.63436693 0.43152455]
----------------------------------------
Trial 1025
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=57, random_state=100,
subsample=0.8))])
cv score: [0.70258621 0.69181034 0.61206897 0.64793282 0.42377261]
----------------------------------------
Trial 1026
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='sqrt',
n_estimators=140, random_state=100,
subsample=0.75))])
cv score: [0.52442529 0.68965517 0.54310345 0.64599483 0.44186047]
----------------------------------------
Trial 1027
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=101, random_state=100,
subsample=0.85))])
cv score: [0.6091954 0.61637931 0.40373563 0.69509044 0.44444444]
----------------------------------------
Trial 1028
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
n_estimators=179,
random_state=100))])
cv score: [0.59626437 0.77155172 0.47126437 0.73385013 0.2997416 ]
----------------------------------------
Trial 1029
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74281609 0.63936782 0.70689655 0.63113695 0.4748062 ]
----------------------------------------
Trial 1030
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=78,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 1031
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='sqrt',
n_estimators=26, random_state=100,
subsample=0.85))])
cv score: [0.60488506 0.69252874 0.45833333 0.70801034 0.38888889]
----------------------------------------
Trial 1032
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=164, random_state=100,
subsample=0.65))])
cv score: [0.63505747 0.52729885 0.5387931 0.71059432 0.40826873]
----------------------------------------
Trial 1033
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=148,
random_state=100))])
cv score: [0.60057471 0.67672414 0.60704023 0.72868217 0.40697674]
----------------------------------------
Trial 1034
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=177,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.70258621 0.57902299 0.625323 0.44315245]
----------------------------------------
Trial 1035
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=124,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.65517241 0.49568966 0.55555556 0.43281654]
----------------------------------------
Trial 1036
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=13,
random_state=100))])
cv score: [0.59626437 0.54741379 0.52945402 0.64405685 0.4496124 ]
----------------------------------------
Trial 1037
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=146,
random_state=100))])
cv score: [0.66235632 0.63936782 0.64224138 0.64857881 0.45090439]
----------------------------------------
Trial 1038
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=17, random_state=100))])
cv score: [0.52514368 0.66307471 0.46336207 0.57299742 0.58268734]
----------------------------------------
Trial 1039
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=61,
random_state=100))])
cv score: [0.61350575 0.63433908 0.60488506 0.67958656 0.36950904]
----------------------------------------
Trial 1040
----------------------------------------
Parameters {'gb__n_estimators': 55, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=55, random_state=100,
subsample=0.95))])
cv score: [0.70545977 0.64798851 0.70402299 0.66666667 0.44315245]
----------------------------------------
Trial 1041
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=17,
random_state=100))])
cv score: [0.64152299 0.59913793 0.56034483 0.51744186 0.41860465]
----------------------------------------
Trial 1042
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=136,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.48850575 0.60344828 0.56609195 0.54780362 0.43023256]
----------------------------------------
Trial 1043
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=194, random_state=100,
subsample=0.65))])
cv score: [0.62787356 0.74712644 0.48275862 0.58397933 0.49354005]
----------------------------------------
Trial 1044
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=22, random_state=100,
subsample=0.85))])
cv score: [0.53735632 0.47844828 0.5158046 0.73901809 0.4754522 ]
----------------------------------------
Trial 1045
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76867816 0.63074713 0.66522989 0.62790698 0.45607235]
----------------------------------------
Trial 1046
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='sqrt',
n_estimators=115, random_state=100,
subsample=0.95))])
cv score: [0.57614943 0.71408046 0.5158046 0.64082687 0.46899225]
----------------------------------------
Trial 1047
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='sqrt',
n_estimators=154, random_state=100,
subsample=0.65))])
cv score: [0.70545977 0.63505747 0.66954023 0.63307494 0.4121447 ]
----------------------------------------
Trial 1048
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=123, random_state=100))])
cv score: [0.75 0.63649425 0.66954023 0.57364341 0.40180879]
----------------------------------------
Trial 1049
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=104, random_state=100))])
cv score: [0.61494253 0.71192529 0.64224138 0.67054264 0.4250646 ]
----------------------------------------
Trial 1050
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=80,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.63505747 0.58477011 0.52713178 0.49612403]
----------------------------------------
Trial 1051
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=33,
random_state=100))])
cv score: [0.58477011 0.68534483 0.5545977 0.64470284 0.50516796]
----------------------------------------
Trial 1052
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=139, random_state=100))])
cv score: [0.65229885 0.67528736 0.62212644 0.59431525 0.43281654]
----------------------------------------
Trial 1053
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
n_estimators=180, random_state=100,
subsample=0.75))])
cv score: [0.57902299 0.66235632 0.54310345 0.48062016 0.50387597]
----------------------------------------
Trial 1054
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.70114943 0.57471264 0.61111111 0.43927649]
----------------------------------------
Trial 1055
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
n_estimators=164, random_state=100,
subsample=0.8))])
cv score: [0.53017241 0.77586207 0.58189655 0.60594315 0.47157623]
----------------------------------------
Trial 1056
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=91,
random_state=100))])
cv score: [0.61494253 0.69109195 0.61781609 0.71963824 0.39018088]
----------------------------------------
Trial 1057
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=163, random_state=100))])
cv score: [0.60632184 0.70258621 0.67097701 0.69509044 0.44056848]
----------------------------------------
Trial 1058
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=137, random_state=100,
subsample=0.75))])
cv score: [0.5545977 0.66666667 0.64224138 0.6124031 0.41343669]
----------------------------------------
Trial 1059
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=185, random_state=100))])
cv score: [0.57902299 0.6875 0.60632184 0.60852713 0.43281654]
----------------------------------------
Trial 1060
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=77,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7341954 0.57902299 0.69755747 0.65697674 0.43152455]
----------------------------------------
Trial 1061
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=124,
random_state=100))])
cv score: [0.61350575 0.59841954 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 1062
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
n_estimators=54, random_state=100,
subsample=0.9))])
cv score: [0.69755747 0.67313218 0.59841954 0.62273902 0.40826873]
----------------------------------------
Trial 1063
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=18,
random_state=100))])
cv score: [0.73994253 0.62787356 0.55747126 0.7248062 0.44832041]
----------------------------------------
Trial 1064
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=25, random_state=100,
subsample=0.85))])
cv score: [0.59770115 0.64367816 0.60632184 0.57105943 0.38113695]
----------------------------------------
Trial 1065
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=106,
random_state=100))])
cv score: [0.64008621 0.59770115 0.56178161 0.51744186 0.41860465]
----------------------------------------
Trial 1066
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=31,
random_state=100))])
cv score: [0.66020115 0.60632184 0.65373563 0.5503876 0.29780362]
----------------------------------------
Trial 1067
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=93,
random_state=100))])
cv score: [0.57183908 0.64511494 0.58908046 0.76485788 0.41731266]
----------------------------------------
Trial 1068
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=126, random_state=100,
subsample=0.95))])
cv score: [0.61925287 0.6795977 0.56896552 0.69638243 0.47674419]
----------------------------------------
Trial 1069
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=79, random_state=100,
subsample=0.75))])
cv score: [0.67672414 0.65229885 0.52298851 0.67829457 0.41860465]
----------------------------------------
Trial 1070
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=91,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.70689655 0.50862069 0.63307494 0.41989664]
----------------------------------------
Trial 1071
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, n_estimators=98,
random_state=100, subsample=0.9))])
cv score: [0.55890805 0.68678161 0.54310345 0.67700258 0.48449612]
----------------------------------------
Trial 1072
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=182,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.71264368 0.60632184 0.55684755 0.37080103]
----------------------------------------
Trial 1073
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=178,
random_state=100))])
cv score: [0.56321839 0.61063218 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 1074
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='sqrt',
n_estimators=174,
random_state=100))])
cv score: [0.65086207 0.73275862 0.52298851 0.57881137 0.50516796]
----------------------------------------
Trial 1075
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=188, random_state=100))])
cv score: [0.65517241 0.67241379 0.59051724 0.68217054 0.38501292]
----------------------------------------
Trial 1076
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=145,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.63074713 0.56752874 0.625323 0.45736434]
----------------------------------------
Trial 1077
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=66,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.62931034 0.69252874 0.6369509 0.43540052]
----------------------------------------
Trial 1078
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=61,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.71264368 0.66091954 0.63824289 0.41860465]
----------------------------------------
Trial 1079
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=137,
random_state=100))])
cv score: [0.61494253 0.67672414 0.60775862 0.72093023 0.40826873]
----------------------------------------
Trial 1080
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=159, random_state=100))])
cv score: [0.64224138 0.65229885 0.63362069 0.68604651 0.40310078]
----------------------------------------
Trial 1081
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=134,
random_state=100))])
cv score: [0.58692529 0.60272989 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 1082
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
random_state=100, subsample=0.7))])
cv score: [0.56034483 0.62212644 0.5387931 0.60981912 0.53617571]
----------------------------------------
Trial 1083
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=114,
random_state=100))])
cv score: [0.59482759 0.67816092 0.58908046 0.67312661 0.43475452]
----------------------------------------
Trial 1084
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, n_estimators=192,
random_state=100))])
cv score: [0.59482759 0.66810345 0.52873563 0.65762274 0.43152455]
----------------------------------------
Trial 1085
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.68534483 0.53017241 0.55684755 0.41602067]
----------------------------------------
Trial 1086
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=10, random_state=100,
subsample=0.95))])
cv score: [0.63793103 0.60488506 0.52729885 0.60206718 0.42764858]
----------------------------------------
Trial 1087
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=105, random_state=100,
subsample=0.6))])
cv score: [0.65086207 0.56178161 0.42528736 0.58527132 0.45090439]
----------------------------------------
Trial 1088
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='sqrt',
n_estimators=132, random_state=100,
subsample=0.9))])
cv score: [0.49137931 0.8204023 0.52011494 0.52196382 0.44186047]
----------------------------------------
Trial 1089
----------------------------------------
Parameters {'rf__n_estimators': 60, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=60, random_state=100))])
cv score: [0.72701149 0.65086207 0.71408046 0.53875969 0.36692506]
----------------------------------------
Trial 1090
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=195,
random_state=100))])
cv score: [0.61997126 0.59841954 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 1091
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=28, random_state=100,
subsample=0.6))])
cv score: [0.5625 0.46408046 0.49640805 0.42700258 0.44638243]
----------------------------------------
Trial 1092
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=118, random_state=100,
subsample=0.75))])
cv score: [0.74712644 0.63793103 0.62068966 0.68863049 0.45219638]
----------------------------------------
Trial 1093
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=55, random_state=100))])
cv score: [0.57614943 0.61063218 0.65373563 0.64728682 0.38372093]
----------------------------------------
Trial 1094
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=159,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.68821839 0.66522989 0.61757106 0.44573643]
----------------------------------------
Trial 1095
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=159,
random_state=100))])
cv score: [0.59482759 0.67385057 0.62643678 0.68087855 0.43023256]
----------------------------------------
Trial 1096
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53017241 0.69109195 0.73994253 0.62144703 0.37338501]
----------------------------------------
Trial 1097
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=31, random_state=100,
subsample=0.85))])
cv score: [0.78017241 0.63433908 0.73994253 0.66989664 0.35271318]
----------------------------------------
Trial 1098
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=163, random_state=100,
subsample=0.95))])
cv score: [0.52873563 0.71408046 0.47270115 0.63436693 0.47157623]
----------------------------------------
Trial 1099
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=147,
random_state=100))])
cv score: [0.66235632 0.6408046 0.6408046 0.64857881 0.45090439]
----------------------------------------
Trial 1100
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=136, random_state=100))])
cv score: [0.72270115 0.59913793 0.6566092 0.61369509 0.42377261]
----------------------------------------
Trial 1101
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=89,
random_state=100))])
cv score: [0.61637931 0.65229885 0.57614943 0.6627907 0.4121447 ]
----------------------------------------
Trial 1102
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=98, random_state=100,
subsample=0.6))])
cv score: [0.58692529 0.48994253 0.56752874 0.374677 0.44444444]
----------------------------------------
Trial 1103
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=113,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75287356 0.63649425 0.63649425 0.60723514 0.47932817]
----------------------------------------
Trial 1104
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53951149 0.58692529 0.5 0.47093023 0.33139535]
----------------------------------------
Trial 1105
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=160,
random_state=100))])
cv score: [0.5862069 0.68318966 0.61350575 0.71705426 0.39664083]
----------------------------------------
Trial 1106
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=161, random_state=100,
subsample=0.65))])
cv score: [0.48275862 0.65301724 0.58405172 0.2997416 0.69638243]
----------------------------------------
Trial 1107
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=165, random_state=100))])
cv score: [0.72844828 0.62931034 0.65517241 0.61369509 0.41472868]
----------------------------------------
Trial 1108
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.72126437 0.56178161 0.68475452 0.44573643]
----------------------------------------
Trial 1109
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=146,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76939655 0.58764368 0.68318966 0.6369509 0.38372093]
----------------------------------------
Trial 1110
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=177,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76508621 0.60272989 0.6954023 0.62661499 0.4373385 ]
----------------------------------------
Trial 1111
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.62356322 0.56896552 0.49483204 0.45478036]
----------------------------------------
Trial 1112
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=15,
random_state=100, subsample=0.9))])
cv score: [0.65373563 0.69971264 0.33189655 0.63178295 0.53229974]
----------------------------------------
Trial 1113
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=134,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73994253 0.63936782 0.70545977 0.62661499 0.45155039]
----------------------------------------
Trial 1114
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=98, random_state=100))])
cv score: [0.74137931 0.61925287 0.60057471 0.59431525 0.42248062]
----------------------------------------
Trial 1115
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=123, random_state=100,
subsample=0.7))])
cv score: [0.74856322 0.67241379 0.61350575 0.68346253 0.40826873]
----------------------------------------
Trial 1116
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=108,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.74712644 0.53304598 0.64211886 0.45478036]
----------------------------------------
Trial 1117
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=119, random_state=100,
subsample=0.95))])
cv score: [0.71264368 0.67528736 0.60344828 0.6744186 0.43927649]
----------------------------------------
Trial 1118
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77298851 0.58189655 0.48563218 0.58914729 0.38178295]
----------------------------------------
Trial 1119
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=21,
random_state=100))])
cv score: [0.64511494 0.59482759 0.5704023 0.57881137 0.41537468]
----------------------------------------
Trial 1120
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=79,
random_state=100))])
cv score: [0.75 0.63505747 0.71982759 0.6498708 0.41989664]
----------------------------------------
Trial 1121
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73275862 0.67241379 0.71264368 0.63888889 0.44121447]
----------------------------------------
Trial 1122
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=45,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.62931034 0.48706897 0.64211886 0.36046512]
----------------------------------------
Trial 1123
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=17,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54741379 0.75 0.57614943 0.66020672 0.42635659]
----------------------------------------
Trial 1124
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=136,
random_state=100))])
cv score: [0.77011494 0.63649425 0.67313218 0.64082687 0.39534884]
----------------------------------------
Trial 1125
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='sqrt',
n_estimators=97, random_state=100,
subsample=0.75))])
cv score: [0.65948276 0.6566092 0.62212644 0.65633075 0.41989664]
----------------------------------------
Trial 1126
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=126,
random_state=100))])
cv score: [0.5545977 0.65948276 0.59195402 0.73901809 0.42571059]
----------------------------------------
Trial 1127
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=62, random_state=100,
subsample=0.65))])
cv score: [0.38936782 0.62787356 0.78591954 0.54005168 0.37855297]
----------------------------------------
Trial 1128
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, n_estimators=180,
random_state=100, subsample=0.9))])
cv score: [0.65373563 0.71408046 0.5 0.69250646 0.44702842]
----------------------------------------
Trial 1129
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55100575 0.54597701 0.47126437 0.47674419 0.44186047]
----------------------------------------
Trial 1130
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=152,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7808908 0.61206897 0.67816092 0.625323 0.39599483]
----------------------------------------
Trial 1131
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=71,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78304598 0.59554598 0.67528736 0.65956072 0.44056848]
----------------------------------------
Trial 1132
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76293103 0.63936782 0.68390805 0.59560724 0.45219638]
----------------------------------------
Trial 1133
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=144,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.6954023 0.48850575 0.64470284 0.37596899]
----------------------------------------
Trial 1134
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=43,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.73132184 0.63649425 0.54909561 0.43152455]
----------------------------------------
Trial 1135
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78304598 0.61135057 0.65086207 0.63565891 0.43927649]
----------------------------------------
Trial 1136
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67025862 0.56034483 0.59913793 0.53423773 0.38049096]
----------------------------------------
Trial 1137
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=85,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.65229885 0.65373563 0.5878553 0.36692506]
----------------------------------------
Trial 1138
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=180, random_state=100,
subsample=0.95))])
cv score: [0.61637931 0.66235632 0.48994253 0.60594315 0.44056848]
----------------------------------------
Trial 1139
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=170,
random_state=100))])
cv score: [0.64224138 0.67385057 0.57758621 0.63824289 0.43023256]
----------------------------------------
Trial 1140
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70258621 0.57614943 0.67672414 0.55232558 0.36692506]
----------------------------------------
Trial 1141
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=59,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.72413793 0.61637931 0.63565891 0.46511628]
----------------------------------------
Trial 1142
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=33, random_state=100,
subsample=0.65))])
cv score: [0.57327586 0.45402299 0.53448276 0.35788114 0.47803618]
----------------------------------------
Trial 1143
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=77, random_state=100,
subsample=0.75))])
cv score: [0.55890805 0.70977011 0.58045977 0.64211886 0.43023256]
----------------------------------------
Trial 1144
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=114, random_state=100,
subsample=0.9))])
cv score: [0.57471264 0.63936782 0.5 0.61498708 0.44186047]
----------------------------------------
Trial 1145
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72126437 0.68678161 0.68965517 0.5994832 0.42571059]
----------------------------------------
Trial 1146
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=189, random_state=100,
subsample=0.85))])
cv score: [0.61494253 0.7112069 0.53304598 0.60206718 0.56718346]
----------------------------------------
Trial 1147
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=107,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52586207 0.68965517 0.55028736 0.54263566 0.39664083]
----------------------------------------
Trial 1148
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=186, random_state=100,
subsample=0.95))])
cv score: [0.52442529 0.38793103 0.55172414 0.65891473 0.54263566]
----------------------------------------
Trial 1149
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77873563 0.62068966 0.64942529 0.62144703 0.46640827]
----------------------------------------
Trial 1150
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='sqrt',
n_estimators=66, random_state=100,
subsample=0.65))])
cv score: [0.61494253 0.6637931 0.51436782 0.60465116 0.45478036]
----------------------------------------
Trial 1151
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
n_estimators=86, random_state=100,
subsample=0.9))])
cv score: [0.60344828 0.69683908 0.50287356 0.65116279 0.45607235]
----------------------------------------
Trial 1152
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=72,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78735632 0.6566092 0.6795977 0.64018088 0.44702842]
----------------------------------------
Trial 1153
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=81, random_state=100,
subsample=0.75))])
cv score: [0.67816092 0.66522989 0.65517241 0.61498708 0.4121447 ]
----------------------------------------
Trial 1154
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, n_estimators=190,
random_state=100,
subsample=0.65))])
cv score: [0.61206897 0.67816092 0.50574713 0.67054264 0.48320413]
----------------------------------------
Trial 1155
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.54741379 0.66810345 0.60775862 0.62144703 0.43669251]
----------------------------------------
Trial 1156
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='log2',
n_estimators=22, random_state=100,
subsample=0.95))])
cv score: [0.66810345 0.66666667 0.65373563 0.6744186 0.48966408]
----------------------------------------
Trial 1157
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=174, random_state=100))])
cv score: [0.63649425 0.64942529 0.58764368 0.6744186 0.37726098]
----------------------------------------
Trial 1158
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.65229885 0.63649425 0.57751938 0.39147287]
----------------------------------------
Trial 1159
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=36,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69109195 0.66954023 0.67672414 0.63501292 0.43540052]
----------------------------------------
Trial 1160
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=145,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76795977 0.58045977 0.71048851 0.60206718 0.4244186 ]
----------------------------------------
Trial 1161
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.61063218 0.61781609 0.62273902 0.40568475]
----------------------------------------
Trial 1162
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.58764368 0.66522989 0.65891473 0.44832041]
----------------------------------------
Trial 1163
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=110,
random_state=100))])
cv score: [0.64367816 0.66954023 0.61350575 0.69509044 0.41860465]
----------------------------------------
Trial 1164
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61781609 0.67385057 0.66954023 0.61757106 0.43410853]
----------------------------------------
Trial 1165
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=140,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70833333 0.59195402 0.46551724 0.51614987 0.37661499]
----------------------------------------
Trial 1166
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=178,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77442529 0.59770115 0.7112069 0.57622739 0.44121447]
----------------------------------------
Trial 1167
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=129, random_state=100))])
cv score: [0.76436782 0.62931034 0.67097701 0.63824289 0.41602067]
----------------------------------------
Trial 1168
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=140,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72270115 0.6566092 0.71551724 0.63630491 0.41602067]
----------------------------------------
Trial 1169
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=51, random_state=100,
subsample=0.85))])
cv score: [0.56034483 0.64655172 0.53735632 0.66537468 0.44702842]
----------------------------------------
Trial 1170
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75287356 0.6566092 0.69109195 0.61498708 0.44896641]
----------------------------------------
Trial 1171
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=194,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67816092 0.65301724 0.58836207 0.55555556 0.36757106]
----------------------------------------
Trial 1172
----------------------------------------
Parameters {'gb__n_estimators': 177, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
n_estimators=177, random_state=100,
subsample=0.9))])
cv score: [0.625 0.63649425 0.50143678 0.65633075 0.38113695]
----------------------------------------
Trial 1173
----------------------------------------
Parameters {'xgb__n_estimators': 95, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=95,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7112069 0.64367816 0.68534483 0.6621447 0.38630491]
----------------------------------------
Trial 1174
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=108, random_state=100,
subsample=0.8))])
cv score: [0.69827586 0.66522989 0.65445402 0.67377261 0.38501292]
----------------------------------------
Trial 1175
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=119, random_state=100))])
cv score: [0.5704023 0.66235632 0.59626437 0.63178295 0.44056848]
----------------------------------------
Trial 1176
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=66,
random_state=100))])
cv score: [0.69396552 0.63218391 0.67528736 0.65503876 0.37726098]
----------------------------------------
Trial 1177
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=190,
random_state=100))])
cv score: [0.57327586 0.59482759 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 1178
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='log2',
n_estimators=67, random_state=100,
subsample=0.8))])
cv score: [0.61206897 0.70258621 0.56034483 0.59173127 0.43410853]
----------------------------------------
Trial 1179
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=116,
random_state=100, subsample=0.7))])
cv score: [0.70977011 0.61494253 0.60775862 0.7622739 0.5 ]
----------------------------------------
Trial 1180
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=23, random_state=100,
subsample=0.7))])
cv score: [0.6091954 0.65517241 0.56034483 0.71317829 0.40826873]
----------------------------------------
Trial 1181
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=57, random_state=100))])
cv score: [0.69396552 0.61206897 0.57183908 0.59431525 0.42635659]
----------------------------------------
Trial 1182
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=199,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.69827586 0.59482759 0.62144703 0.45607235]
----------------------------------------
Trial 1183
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=83, random_state=100,
subsample=0.75))])
cv score: [0.48132184 0.45114943 0.39655172 0.45736434 0.52325581]
----------------------------------------
Trial 1184
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=18,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54741379 0.65948276 0.52442529 0.57105943 0.43927649]
----------------------------------------
Trial 1185
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74353448 0.59698276 0.69181034 0.60788114 0.41666667]
----------------------------------------
Trial 1186
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=167, random_state=100,
subsample=0.75))])
cv score: [0.68965517 0.67385057 0.58477011 0.64599483 0.44186047]
----------------------------------------
Trial 1187
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=35, random_state=100,
subsample=0.85))])
cv score: [0.6408046 0.60488506 0.52442529 0.59689922 0.4625323 ]
----------------------------------------
Trial 1188
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=186, random_state=100,
subsample=0.8))])
cv score: [0.64798851 0.65229885 0.60201149 0.66537468 0.49354005]
----------------------------------------
Trial 1189
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=199, random_state=100,
subsample=0.75))])
cv score: [0.6637931 0.66235632 0.68678161 0.67054264 0.43669251]
----------------------------------------
Trial 1190
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=151,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72844828 0.67097701 0.64367816 0.65116279 0.44573643]
----------------------------------------
Trial 1191
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=82, random_state=100))])
cv score: [0.60201149 0.67241379 0.57614943 0.5994832 0.43346253]
----------------------------------------
Trial 1192
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=23,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77586207 0.59267241 0.62571839 0.54909561 0.39728682]
----------------------------------------
Trial 1193
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=31, random_state=100))])
cv score: [0.5704023 0.6566092 0.45905172 0.6505168 0.46511628]
----------------------------------------
Trial 1194
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=83,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72844828 0.61637931 0.71695402 0.61563307 0.41602067]
----------------------------------------
Trial 1195
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=15,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54166667 0.60057471 0.54454023 0.61886305 0.49612403]
----------------------------------------
Trial 1196
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=26,
random_state=100))])
cv score: [0.68031609 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 1197
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=141, random_state=100))])
cv score: [0.74856322 0.64942529 0.67241379 0.65245478 0.42764858]
----------------------------------------
Trial 1198
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=70,
random_state=100))])
cv score: [0.73275862 0.64655172 0.70545977 0.65245478 0.40826873]
----------------------------------------
Trial 1199
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=171, random_state=100))])
cv score: [0.5704023 0.64224138 0.57614943 0.64082687 0.42377261]
----------------------------------------
Trial 1200
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=111, random_state=100,
subsample=0.9))])
cv score: [0.63074713 0.69827586 0.52729885 0.61627907 0.42635659]
----------------------------------------
Trial 1201
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56321839 0.72413793 0.62068966 0.59431525 0.38888889]
----------------------------------------
Trial 1202
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=145, random_state=100))])
cv score: [0.61350575 0.69971264 0.66810345 0.67571059 0.42571059]
----------------------------------------
Trial 1203
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=50, random_state=100))])
cv score: [0.70114943 0.6795977 0.58333333 0.62790698 0.39793282]
----------------------------------------
Trial 1204
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=83, random_state=100,
subsample=0.85))])
cv score: [0.6637931 0.67528736 0.65229885 0.62144703 0.41343669]
----------------------------------------
Trial 1205
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=84,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.7341954 0.52586207 0.61369509 0.45090439]
----------------------------------------
Trial 1206
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=58, random_state=100))])
cv score: [0.72844828 0.64367816 0.71695402 0.55297158 0.37080103]
----------------------------------------
Trial 1207
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=65,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66666667 0.54597701 0.57183908 0.55297158 0.4005168 ]
----------------------------------------
Trial 1208
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=186, random_state=100,
subsample=0.95))])
cv score: [0.60632184 0.72413793 0.55747126 0.65503876 0.42377261]
----------------------------------------
Trial 1209
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.7183908 0.63074713 0.63824289 0.41472868]
----------------------------------------
Trial 1210
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=131, random_state=100,
subsample=0.6))])
cv score: [0.60344828 0.70689655 0.57183908 0.70155039 0.49095607]
----------------------------------------
Trial 1211
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='sqrt',
n_estimators=166, random_state=100,
subsample=0.85))])
cv score: [0.59626437 0.65086207 0.52011494 0.62661499 0.40697674]
----------------------------------------
Trial 1212
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77442529 0.5862069 0.71264368 0.55620155 0.43863049]
----------------------------------------
Trial 1213
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=25, random_state=100,
subsample=0.7))])
cv score: [0.62068966 0.6954023 0.54310345 0.62403101 0.53100775]
----------------------------------------
Trial 1214
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=101, random_state=100,
subsample=0.6))])
cv score: [0.43031609 0.46336207 0.57902299 0.64599483 0.45090439]
----------------------------------------
Trial 1215
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=152,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64942529 0.68534483 0.67672414 0.6369509 0.40568475]
----------------------------------------
Trial 1216
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=32,
random_state=100,
subsample=0.85))])
cv score: [0.57758621 0.72270115 0.52442529 0.62661499 0.37338501]
----------------------------------------
Trial 1217
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60201149 0.74137931 0.60632184 0.59302326 0.4121447 ]
----------------------------------------
Trial 1218
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=107, random_state=100,
subsample=0.95))])
cv score: [0.63362069 0.65948276 0.5316092 0.61627907 0.45865633]
----------------------------------------
Trial 1219
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=146,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.69971264 0.56896552 0.63307494 0.40310078]
----------------------------------------
Trial 1220
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.66522989 0.625 0.57881137 0.46511628]
----------------------------------------
Trial 1221
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=44, random_state=100,
subsample=0.95))])
cv score: [0.70689655 0.7112069 0.67672414 0.6873385 0.43410853]
----------------------------------------
Trial 1222
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.70833333 0.63074713 0.60465116 0.43281654]
----------------------------------------
Trial 1223
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.8))])
cv score: [0.75287356 0.63074713 0.68534483 0.63824289 0.42635659]
----------------------------------------
Trial 1224
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.65373563 0.65948276 0.55684755 0.42118863]
----------------------------------------
Trial 1225
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69109195 0.68534483 0.63649425 0.63307494 0.44315245]
----------------------------------------
Trial 1226
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=48,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63218391 0.73994253 0.62356322 0.62919897 0.47932817]
----------------------------------------
Trial 1227
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=110, random_state=100,
subsample=0.9))])
cv score: [0.49137931 0.6795977 0.4066092 0.5878553 0.37338501]
----------------------------------------
Trial 1228
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53591954 0.67672414 0.59626437 0.61498708 0.41731266]
----------------------------------------
Trial 1229
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='log2',
n_estimators=189, random_state=100,
subsample=0.95))])
cv score: [0.6408046 0.69396552 0.5316092 0.65374677 0.45090439]
----------------------------------------
Trial 1230
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=94, random_state=100,
subsample=0.85))])
cv score: [0.58477011 0.75 0.62356322 0.72222222 0.43927649]
----------------------------------------
Trial 1231
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=188, random_state=100))])
cv score: [0.6408046 0.66666667 0.60201149 0.62403101 0.45994832]
----------------------------------------
Trial 1232
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
n_estimators=69, random_state=100,
subsample=0.8))])
cv score: [0.59626437 0.7658046 0.55172414 0.68217054 0.41602067]
----------------------------------------
Trial 1233
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=48,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66954023 0.66666667 0.71695402 0.66925065 0.46899225]
----------------------------------------
Trial 1234
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=48,
random_state=100))])
cv score: [0.56537356 0.63362069 0.67097701 0.68863049 0.39147287]
----------------------------------------
Trial 1235
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
n_estimators=167, random_state=100,
subsample=0.7))])
cv score: [0.67097701 0.67528736 0.54310345 0.67183463 0.47286822]
----------------------------------------
Trial 1236
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=97,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68965517 0.6566092 0.67241379 0.58914729 0.44702842]
----------------------------------------
Trial 1237
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=175, random_state=100))])
cv score: [0.56178161 0.64942529 0.60991379 0.64082687 0.40245478]
----------------------------------------
Trial 1238
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='log2',
n_estimators=39, random_state=100,
subsample=0.95))])
cv score: [0.56465517 0.73563218 0.65517241 0.66408269 0.41731266]
----------------------------------------
Trial 1239
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=118, random_state=100,
subsample=0.8))])
cv score: [0.69827586 0.70833333 0.53735632 0.5620155 0.53100775]
----------------------------------------
Trial 1240
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=80, random_state=100,
subsample=0.95))])
cv score: [0.50431034 0.73275862 0.58333333 0.65633075 0.47674419]
----------------------------------------
Trial 1241
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=77,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.6566092 0.63936782 0.60465116 0.37984496]
----------------------------------------
Trial 1242
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
n_estimators=184, random_state=100,
subsample=0.7))])
cv score: [0.58836207 0.40948276 0.54885057 0.67312661 0.67958656]
----------------------------------------
Trial 1243
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=93, random_state=100))])
cv score: [0.75718391 0.65804598 0.65804598 0.64211886 0.44832041]
----------------------------------------
Trial 1244
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=108, random_state=100))])
cv score: [0.5862069 0.65948276 0.59626437 0.61369509 0.44056848]
----------------------------------------
Trial 1245
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=68, random_state=100,
subsample=0.85))])
cv score: [0.58908046 0.74712644 0.57902299 0.70801034 0.43927649]
----------------------------------------
Trial 1246
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=10,
random_state=100))])
cv score: [0.5783046 0.60201149 0.51364943 0.52906977 0.46899225]
----------------------------------------
Trial 1247
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57758621 0.64367816 0.63649425 0.60981912 0.42635659]
----------------------------------------
Trial 1248
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.65086207 0.66522989 0.59560724 0.42377261]
----------------------------------------
Trial 1249
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.50718391 0.64224138 0.50862069 0.62919897 0.44444444]
----------------------------------------
Trial 1250
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=141,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 1251
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
n_estimators=176, random_state=100,
subsample=0.9))])
cv score: [0.60201149 0.73706897 0.58908046 0.66925065 0.39534884]
----------------------------------------
Trial 1252
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=169,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68534483 0.67097701 0.63074713 0.63824289 0.43540052]
----------------------------------------
Trial 1253
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.73850575 0.58477011 0.64341085 0.46770026]
----------------------------------------
Trial 1254
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
n_estimators=116, random_state=100,
subsample=0.75))])
cv score: [0.63362069 0.67241379 0.5862069 0.6744186 0.42764858]
----------------------------------------
Trial 1255
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.72701149 0.63074713 0.62790698 0.43281654]
----------------------------------------
Trial 1256
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=165, random_state=100))])
cv score: [0.74137931 0.63649425 0.6637931 0.58139535 0.40439276]
----------------------------------------
Trial 1257
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=167,
random_state=100))])
cv score: [0.61781609 0.66666667 0.61494253 0.67700258 0.47286822]
----------------------------------------
Trial 1258
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=22,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66020115 0.6875 0.70905172 0.58074935 0.38242894]
----------------------------------------
Trial 1259
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=147, random_state=100))])
cv score: [0.75862069 0.63074713 0.68103448 0.62790698 0.40826873]
----------------------------------------
Trial 1260
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=132, random_state=100))])
cv score: [0.57327586 0.6875 0.61853448 0.60852713 0.4373385 ]
----------------------------------------
Trial 1261
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=178, random_state=100))])
cv score: [0.73132184 0.6408046 0.64942529 0.64728682 0.40956072]
----------------------------------------
Trial 1262
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=170,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.59770115 0.66235632 0.56136951 0.34625323]
----------------------------------------
Trial 1263
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=61,
random_state=100))])
cv score: [0.61350575 0.63433908 0.60488506 0.67958656 0.36950904]
----------------------------------------
Trial 1264
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=98, random_state=100))])
cv score: [0.59051724 0.69755747 0.63505747 0.65503876 0.44379845]
----------------------------------------
Trial 1265
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=75,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.64798851 0.52586207 0.53100775 0.35271318]
----------------------------------------
Trial 1266
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=168,
random_state=100, subsample=0.9))])
cv score: [0.54741379 0.74281609 0.54885057 0.57751938 0.40310078]
----------------------------------------
Trial 1267
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=149, random_state=100))])
cv score: [0.5545977 0.66091954 0.61422414 0.61627907 0.42958656]
----------------------------------------
Trial 1268
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=89,
random_state=100))])
cv score: [0.66810345 0.63074713 0.4454023 0.51937984 0.4379845 ]
----------------------------------------
Trial 1269
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=171,
random_state=100))])
cv score: [0.61278736 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 1270
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=143, random_state=100,
subsample=0.8))])
cv score: [0.64942529 0.68965517 0.52011494 0.68475452 0.39664083]
----------------------------------------
Trial 1271
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=196, random_state=100))])
cv score: [0.57974138 0.68534483 0.58477011 0.62984496 0.44509044]
----------------------------------------
Trial 1272
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=174, random_state=100))])
cv score: [0.77155172 0.63505747 0.5704023 0.625323 0.40374677]
----------------------------------------
Trial 1273
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=11,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.5545977 0.55890805 0.48320413 0.42958656]
----------------------------------------
Trial 1274
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=151,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 1275
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=43,
random_state=100))])
cv score: [0.64008621 0.60560345 0.56034483 0.51744186 0.41860465]
----------------------------------------
Trial 1276
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=95, random_state=100))])
cv score: [0.61925287 0.63362069 0.62787356 0.57364341 0.41472868]
----------------------------------------
Trial 1277
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=133,
random_state=100))])
cv score: [0.77442529 0.6408046 0.68031609 0.63953488 0.40180879]
----------------------------------------
Trial 1278
----------------------------------------
Parameters {'rf__n_estimators': 125, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=125,
random_state=100))])
cv score: [0.77873563 0.63362069 0.66738506 0.63565891 0.39276486]
----------------------------------------
Trial 1279
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=154,
random_state=100, subsample=0.8))])
cv score: [0.55028736 0.72557471 0.62356322 0.64470284 0.42377261]
----------------------------------------
Trial 1280
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=22, random_state=100,
subsample=0.95))])
cv score: [0.54022989 0.56321839 0.41091954 0.62273902 0.49224806]
----------------------------------------
Trial 1281
----------------------------------------
Parameters {'rf__n_estimators': 25, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=25,
random_state=100))])
cv score: [0.55747126 0.55603448 0.51939655 0.67183463 0.42958656]
----------------------------------------
Trial 1282
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=59,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.82112069 0.6408046 0.67025862 0.63888889 0.4496124 ]
----------------------------------------
Trial 1283
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='log2',
n_estimators=57, random_state=100,
subsample=0.6))])
cv score: [0.68534483 0.63218391 0.57183908 0.53488372 0.55684755]
----------------------------------------
Trial 1284
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.6566092 0.66522989 0.62144703 0.38888889]
----------------------------------------
Trial 1285
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=118, random_state=100,
subsample=0.65))])
cv score: [0.65373563 0.6954023 0.49856322 0.67054264 0.47674419]
----------------------------------------
Trial 1286
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=65,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.70689655 0.62356322 0.46640827 0.4625323 ]
----------------------------------------
Trial 1287
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=53, random_state=100))])
cv score: [0.625 0.69396552 0.67672414 0.68346253 0.42635659]
----------------------------------------
Trial 1288
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=84,
random_state=100))])
cv score: [0.66235632 0.72701149 0.56896552 0.55813953 0.50129199]
----------------------------------------
Trial 1289
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.68965517 0.73850575 0.62661499 0.34819121]
----------------------------------------
Trial 1290
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.67528736 0.67385057 0.61498708 0.39793282]
----------------------------------------
Trial 1291
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72413793 0.59482759 0.47126437 0.56459948 0.39470284]
----------------------------------------
Trial 1292
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=14, random_state=100,
subsample=0.7))])
cv score: [0.48994253 0.71408046 0.44827586 0.61111111 0.4379845 ]
----------------------------------------
Trial 1293
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=195, random_state=100,
subsample=0.65))])
cv score: [0.61063218 0.51293103 0.51724138 0.74289406 0.39405685]
----------------------------------------
Trial 1294
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=170,
random_state=100))])
cv score: [0.54454023 0.65732759 0.58045977 0.73901809 0.41925065]
----------------------------------------
Trial 1295
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=34, random_state=100,
subsample=0.65))])
cv score: [0.60057471 0.63649425 0.54310345 0.71059432 0.45736434]
----------------------------------------
Trial 1296
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
n_estimators=92,
random_state=100))])
cv score: [0.40732759 0.54382184 0.56034483 0.55620155 0.36627907]
----------------------------------------
Trial 1297
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=98, random_state=100,
subsample=0.75))])
cv score: [0.57327586 0.64367816 0.50287356 0.67312661 0.50129199]
----------------------------------------
Trial 1298
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.71264368 0.61350575 0.66020672 0.44573643]
----------------------------------------
Trial 1299
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=166,
random_state=100))])
cv score: [0.72557471 0.64798851 0.59051724 0.6369509 0.42377261]
----------------------------------------
Trial 1300
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='sqrt',
n_estimators=119, random_state=100,
subsample=0.75))])
cv score: [0.57614943 0.55028736 0.62068966 0.5878553 0.46382429]
----------------------------------------
Trial 1301
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=112,
random_state=100))])
cv score: [0.68031609 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 1302
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=195,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.63218391 0.70545977 0.54909561 0.44315245]
----------------------------------------
Trial 1303
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55316092 0.6954023 0.5933908 0.52196382 0.48514212]
----------------------------------------
Trial 1304
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='sqrt',
n_estimators=133, random_state=100,
subsample=0.9))])
cv score: [0.63505747 0.67528736 0.3908046 0.65762274 0.43410853]
----------------------------------------
Trial 1305
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=89, random_state=100))])
cv score: [0.56609195 0.67385057 0.56681034 0.60142119 0.46705426]
----------------------------------------
Trial 1306
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78591954 0.59482759 0.67385057 0.57816537 0.38178295]
----------------------------------------
Trial 1307
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70258621 0.58261494 0.64942529 0.55490956 0.38888889]
----------------------------------------
Trial 1308
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=93,
random_state=100))])
cv score: [0.61350575 0.60057471 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 1309
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=10,
random_state=100))])
cv score: [0.53304598 0.60344828 0.54166667 0.61498708 0.43152455]
----------------------------------------
Trial 1310
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=165, random_state=100))])
cv score: [0.77442529 0.63936782 0.57471264 0.62661499 0.3998708 ]
----------------------------------------
Trial 1311
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70258621 0.62212644 0.63793103 0.62661499 0.42248062]
----------------------------------------
Trial 1312
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
n_estimators=120, random_state=100,
subsample=0.65))])
cv score: [0.53591954 0.75287356 0.62356322 0.58527132 0.46640827]
----------------------------------------
Trial 1313
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=13, random_state=100))])
cv score: [0.58333333 0.65229885 0.47916667 0.55943152 0.40503876]
----------------------------------------
Trial 1314
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=154,
random_state=100))])
cv score: [0.77873563 0.63505747 0.68390805 0.63953488 0.39664083]
----------------------------------------
Trial 1315
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.61637931 0.58189655 0.53617571 0.4496124 ]
----------------------------------------
Trial 1316
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65948276 0.69396552 0.65086207 0.5878553 0.45865633]
----------------------------------------
Trial 1317
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
n_estimators=123, random_state=100,
subsample=0.6))])
cv score: [0.48132184 0.59626437 0.50287356 0.58656331 0.58656331]
----------------------------------------
Trial 1318
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79238506 0.6329023 0.70043103 0.63824289 0.44573643]
----------------------------------------
Trial 1319
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=75, random_state=100,
subsample=0.8))])
cv score: [0.50862069 0.57614943 0.58333333 0.67312661 0.3501292 ]
----------------------------------------
Trial 1320
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=41, random_state=100,
subsample=0.7))])
cv score: [0.68965517 0.5933908 0.61781609 0.61886305 0.49354005]
----------------------------------------
Trial 1321
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=165, random_state=100,
subsample=0.65))])
cv score: [0.60344828 0.68247126 0.61925287 0.67700258 0.44832041]
----------------------------------------
Trial 1322
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=57, random_state=100,
subsample=0.8))])
cv score: [0.57471264 0.46695402 0.49281609 0.63049096 0.47157623]
----------------------------------------
Trial 1323
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=197, random_state=100))])
cv score: [0.64511494 0.6637931 0.59626437 0.61757106 0.45478036]
----------------------------------------
Trial 1324
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=67, random_state=100))])
cv score: [0.73275862 0.63218391 0.70833333 0.5620155 0.37338501]
----------------------------------------
Trial 1325
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=45,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.77729885 0.50143678 0.48966408 0.45607235]
----------------------------------------
Trial 1326
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=69, random_state=100,
subsample=0.65))])
cv score: [0.51005747 0.66163793 0.48563218 0.29715762 0.69638243]
----------------------------------------
Trial 1327
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=133, random_state=100))])
cv score: [0.55028736 0.67385057 0.61637931 0.62661499 0.39276486]
----------------------------------------
Trial 1328
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=37, random_state=100))])
cv score: [0.72413793 0.67528736 0.6091954 0.6124031 0.43152455]
----------------------------------------
Trial 1329
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.83836207 0.60344828 0.61422414 0.56007752 0.39405685]
----------------------------------------
Trial 1330
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=53,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.57758621 0.5862069 0.48966408 0.36434109]
----------------------------------------
Trial 1331
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=35,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53448276 0.69971264 0.45977011 0.54005168 0.37726098]
----------------------------------------
Trial 1332
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.68678161 0.58333333 0.6627907 0.42635659]
----------------------------------------
Trial 1333
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.56178161 0.64942529 0.61369509 0.45348837]
----------------------------------------
Trial 1334
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=191, random_state=100))])
cv score: [0.71982759 0.64224138 0.65373563 0.58656331 0.41860465]
----------------------------------------
Trial 1335
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.81537356 0.54597701 0.66882184 0.62144703 0.47351421]
----------------------------------------
Trial 1336
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57758621 0.70114943 0.60632184 0.6124031 0.42635659]
----------------------------------------
Trial 1337
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.64224138 0.58189655 0.61369509 0.40310078]
----------------------------------------
Trial 1338
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=98, random_state=100,
subsample=0.75))])
cv score: [0.60344828 0.64511494 0.54166667 0.65116279 0.45736434]
----------------------------------------
Trial 1339
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.65))])
cv score: [0.60775862 0.55603448 0.6091954 0.61111111 0.52842377]
----------------------------------------
Trial 1340
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=105, random_state=100))])
cv score: [0.61350575 0.67385057 0.59626437 0.625323 0.44186047]
----------------------------------------
Trial 1341
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='log2',
n_estimators=36, random_state=100,
subsample=0.6))])
cv score: [0.7183908 0.61925287 0.70689655 0.60465116 0.45478036]
----------------------------------------
Trial 1342
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=43,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.65373563 0.65086207 0.64599483 0.4373385 ]
----------------------------------------
Trial 1343
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=136, random_state=100,
subsample=0.9))])
cv score: [0.74712644 0.64367816 0.66666667 0.65374677 0.4005168 ]
----------------------------------------
Trial 1344
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='sqrt',
n_estimators=18, random_state=100,
subsample=0.95))])
cv score: [0.61637931 0.71982759 0.71264368 0.62403101 0.47674419]
----------------------------------------
Trial 1345
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.67528736 0.56465517 0.59819121 0.44186047]
----------------------------------------
Trial 1346
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=192,
random_state=100))])
cv score: [0.58477011 0.66954023 0.61781609 0.68087855 0.41343669]
----------------------------------------
Trial 1347
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=99,
random_state=100))])
cv score: [0.65229885 0.6408046 0.63649425 0.63049096 0.43540052]
----------------------------------------
Trial 1348
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.7183908 0.61206897 0.63436693 0.42764858]
----------------------------------------
Trial 1349
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=132, random_state=100,
subsample=0.6))])
cv score: [0.6637931 0.61637931 0.54166667 0.60206718 0.4379845 ]
----------------------------------------
Trial 1350
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=162, random_state=100,
subsample=0.7))])
cv score: [0.62356322 0.68678161 0.53591954 0.66408269 0.54909561]
----------------------------------------
Trial 1351
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
n_estimators=25, random_state=100,
subsample=0.95))])
cv score: [0.57327586 0.66954023 0.52873563 0.81136951 0.34237726]
----------------------------------------
Trial 1352
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=111,
random_state=100))])
cv score: [0.70977011 0.64942529 0.67097701 0.65762274 0.41472868]
----------------------------------------
Trial 1353
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='sqrt',
n_estimators=17, random_state=100,
subsample=0.8))])
cv score: [0.63649425 0.61494253 0.31609195 0.63049096 0.45219638]
----------------------------------------
Trial 1354
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=64,
random_state=100))])
cv score: [0.52729885 0.7183908 0.56178161 0.66020672 0.48062016]
----------------------------------------
Trial 1355
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=80, random_state=100))])
cv score: [0.68821839 0.68247126 0.59770115 0.6369509 0.42118863]
----------------------------------------
Trial 1356
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=122,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.64798851 0.61206897 0.58010336 0.4250646 ]
----------------------------------------
Trial 1357
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=109, random_state=100))])
cv score: [0.55890805 0.69252874 0.61206897 0.60335917 0.43927649]
----------------------------------------
Trial 1358
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.60344828 0.55028736 0.54005168 0.41731266]
----------------------------------------
Trial 1359
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=147, random_state=100))])
cv score: [0.5625 0.68031609 0.60201149 0.59431525 0.45284238]
----------------------------------------
Trial 1360
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=156,
random_state=100))])
cv score: [0.58333333 0.68821839 0.57902299 0.68475452 0.41602067]
----------------------------------------
Trial 1361
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=109, random_state=100,
subsample=0.9))])
cv score: [0.79166667 0.59770115 0.72054598 0.64211886 0.36046512]
----------------------------------------
Trial 1362
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=132, random_state=100,
subsample=0.6))])
cv score: [0.65517241 0.63793103 0.55747126 0.60594315 0.47932817]
----------------------------------------
Trial 1363
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
n_estimators=19,
random_state=100))])
cv score: [0.65229885 0.66235632 0.6091954 0.64211886 0.40245478]
----------------------------------------
Trial 1364
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.6))])
cv score: [0.47126437 0.65804598 0.57255747 0.59043928 0.5878553 ]
----------------------------------------
Trial 1365
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=144, random_state=100,
subsample=0.7))])
cv score: [0.54022989 0.73706897 0.60057471 0.70671835 0.45736434]
----------------------------------------
Trial 1366
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.9))])
cv score: [0.56752874 0.65229885 0.56465517 0.67700258 0.51033592]
----------------------------------------
Trial 1367
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=69,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.61781609 0.66091954 0.62661499 0.43410853]
----------------------------------------
Trial 1368
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=45,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.77729885 0.54885057 0.62790698 0.39276486]
----------------------------------------
Trial 1369
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67241379 0.61925287 0.64511494 0.59173127 0.4496124 ]
----------------------------------------
Trial 1370
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=68,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.56178161 0.5387931 0.56976744 0.43410853]
----------------------------------------
Trial 1371
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=141,
random_state=100))])
cv score: [0.65876437 0.52945402 0.55316092 0.72157623 0.38242894]
----------------------------------------
Trial 1372
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.6))])
cv score: [0.73994253 0.63793103 0.69971264 0.62273902 0.39405685]
----------------------------------------
Trial 1373
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=74, random_state=100))])
cv score: [0.72270115 0.60632184 0.57758621 0.57881137 0.43023256]
----------------------------------------
Trial 1374
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=25,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75071839 0.66020115 0.64583333 0.60917313 0.39857881]
----------------------------------------
Trial 1375
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=136,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73491379 0.56824713 0.66020115 0.54586563 0.39147287]
----------------------------------------
Trial 1376
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.75))])
cv score: [0.54310345 0.59913793 0.45545977 0.52067183 0.47286822]
----------------------------------------
Trial 1377
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=146,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.73132184 0.63649425 0.60465116 0.40180879]
----------------------------------------
Trial 1378
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=96, random_state=100,
subsample=0.9))])
cv score: [0.54597701 0.67528736 0.56034483 0.62661499 0.46382429]
----------------------------------------
Trial 1379
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=80, random_state=100,
subsample=0.65))])
cv score: [0.61494253 0.57758621 0.62140805 0.7118863 0.4870801 ]
----------------------------------------
Trial 1380
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.75))])
cv score: [0.52586207 0.5704023 0.42816092 0.5245478 0.35917313]
----------------------------------------
Trial 1381
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.64798851 0.68534483 0.63049096 0.44186047]
----------------------------------------
Trial 1382
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
n_estimators=107, random_state=100,
subsample=0.7))])
cv score: [0.48132184 0.48994253 0.47126437 0.75968992 0.44573643]
----------------------------------------
Trial 1383
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=48,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.625 0.67528736 0.60201149 0.53488372 0.50129199]
----------------------------------------
Trial 1384
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=27, random_state=100))])
cv score: [0.59482759 0.66954023 0.57183908 0.57105943 0.53359173]
----------------------------------------
Trial 1385
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=174, random_state=100))])
cv score: [0.6566092 0.67528736 0.60344828 0.68087855 0.43410853]
----------------------------------------
Trial 1386
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=164,
random_state=100))])
cv score: [0.64798851 0.66522989 0.57471264 0.64211886 0.43152455]
----------------------------------------
Trial 1387
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=20, random_state=100,
subsample=0.9))])
cv score: [0.71336207 0.67816092 0.39152299 0.70155039 0.37984496]
----------------------------------------
Trial 1388
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=48, random_state=100))])
cv score: [0.58908046 0.63218391 0.5704023 0.56330749 0.47286822]
----------------------------------------
Trial 1389
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=86, random_state=100))])
cv score: [0.68103448 0.68103448 0.59626437 0.64341085 0.42635659]
----------------------------------------
Trial 1390
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='sqrt',
n_estimators=144,
random_state=100))])
cv score: [0.57471264 0.69109195 0.50287356 0.60723514 0.42118863]
----------------------------------------
Trial 1391
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=168, random_state=100))])
cv score: [0.72701149 0.63793103 0.65948276 0.64599483 0.41472868]
----------------------------------------
Trial 1392
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=159, random_state=100,
subsample=0.75))])
cv score: [0.64655172 0.66810345 0.43390805 0.60465116 0.41085271]
----------------------------------------
Trial 1393
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=81, random_state=100,
subsample=0.75))])
cv score: [0.64798851 0.6408046 0.51724138 0.65116279 0.40180879]
----------------------------------------
Trial 1394
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=181, random_state=100))])
cv score: [0.64655172 0.63793103 0.59626437 0.60594315 0.40956072]
----------------------------------------
Trial 1395
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=181, random_state=100,
subsample=0.9))])
cv score: [0.55890805 0.72270115 0.60201149 0.69638243 0.40697674]
----------------------------------------
Trial 1396
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=156, random_state=100,
subsample=0.9))])
cv score: [0.61781609 0.69683908 0.48994253 0.65762274 0.45994832]
----------------------------------------
Trial 1397
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
n_estimators=107, random_state=100,
subsample=0.6))])
cv score: [0.55890805 0.69252874 0.55890805 0.68217054 0.44315245]
----------------------------------------
Trial 1398
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.62356322 0.72126437 0.67054264 0.39793282]
----------------------------------------
Trial 1399
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=34, random_state=100,
subsample=0.6))])
cv score: [0.74712644 0.70545977 0.48994253 0.63824289 0.47674419]
----------------------------------------
Trial 1400
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=189, random_state=100))])
cv score: [0.66522989 0.67385057 0.65517241 0.67958656 0.45865633]
----------------------------------------
Trial 1401
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77945402 0.61637931 0.4762931 0.55167959 0.40697674]
----------------------------------------
Trial 1402
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=83,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75862069 0.5625 0.68678161 0.62855297 0.40503876]
----------------------------------------
Trial 1403
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69252874 0.65804598 0.71982759 0.60465116 0.41860465]
----------------------------------------
Trial 1404
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=149, random_state=100,
subsample=0.75))])
cv score: [0.55890805 0.69109195 0.57327586 0.68992248 0.44444444]
----------------------------------------
Trial 1405
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=122, random_state=100))])
cv score: [0.76724138 0.63074713 0.63505747 0.63307494 0.41602067]
----------------------------------------
Trial 1406
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=176,
random_state=100))])
cv score: [0.58908046 0.67672414 0.61350575 0.67700258 0.41989664]
----------------------------------------
Trial 1407
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=71,
random_state=100))])
cv score: [0.59482759 0.6558908 0.64224138 0.72351421 0.38178295]
----------------------------------------
Trial 1408
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75502874 0.56752874 0.67097701 0.61757106 0.45671835]
----------------------------------------
Trial 1409
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=88, random_state=100))])
cv score: [0.61925287 0.68103448 0.60775862 0.67183463 0.42635659]
----------------------------------------
Trial 1410
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=101, random_state=100,
subsample=0.9))])
cv score: [0.65948276 0.65948276 0.60632184 0.57235142 0.49095607]
----------------------------------------
Trial 1411
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=28, random_state=100))])
cv score: [0.54094828 0.65229885 0.50287356 0.63307494 0.5497416 ]
----------------------------------------
Trial 1412
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=161,
random_state=100,
subsample=0.65))])
cv score: [0.58764368 0.70545977 0.53735632 0.63565891 0.41343669]
----------------------------------------
Trial 1413
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.6566092 0.57614943 0.62919897 0.41860465]
----------------------------------------
Trial 1414
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=34, random_state=100,
subsample=0.8))])
cv score: [0.43390805 0.49425287 0.44971264 0.625323 0.47803618]
----------------------------------------
Trial 1415
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=33,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65158046 0.62931034 0.68247126 0.63565891 0.4250646 ]
----------------------------------------
Trial 1416
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71551724 0.64367816 0.71479885 0.56524548 0.3875969 ]
----------------------------------------
Trial 1417
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=186,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.66235632 0.66810345 0.64082687 0.4379845 ]
----------------------------------------
Trial 1418
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.75))])
cv score: [0.57399425 0.48994253 0.43390805 0.5503876 0.44186047]
----------------------------------------
Trial 1419
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='log2',
n_estimators=16, random_state=100,
subsample=0.85))])
cv score: [0.67672414 0.59195402 0.42816092 0.74418605 0.51679587]
----------------------------------------
Trial 1420
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=72, random_state=100))])
cv score: [0.63793103 0.62212644 0.61063218 0.64470284 0.38113695]
----------------------------------------
Trial 1421
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=108,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80387931 0.58979885 0.73635057 0.56653747 0.37790698]
----------------------------------------
Trial 1422
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=178,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.73994253 0.63218391 0.56589147 0.40956072]
----------------------------------------
Trial 1423
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=119, random_state=100,
subsample=0.6))])
cv score: [0.62787356 0.66954023 0.53448276 0.6369509 0.48449612]
----------------------------------------
Trial 1424
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
n_estimators=126, random_state=100,
subsample=0.85))])
cv score: [0.58045977 0.66235632 0.60775862 0.57881137 0.47028424]
----------------------------------------
Trial 1425
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69396552 0.61494253 0.60632184 0.62790698 0.4121447 ]
----------------------------------------
Trial 1426
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=78,
random_state=100))])
cv score: [0.61278736 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 1427
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=171, random_state=100,
subsample=0.8))])
cv score: [0.65517241 0.69109195 0.51724138 0.67700258 0.39922481]
----------------------------------------
Trial 1428
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=76,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55172414 0.65373563 0.66522989 0.64341085 0.40568475]
----------------------------------------
Trial 1429
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=15, random_state=100))])
cv score: [0.74712644 0.63074713 0.57183908 0.47222222 0.45607235]
----------------------------------------
Trial 1430
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=192,
random_state=100))])
cv score: [0.56321839 0.61063218 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 1431
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.68390805 0.59913793 0.58914729 0.32945736]
----------------------------------------
Trial 1432
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=63, random_state=100,
subsample=0.8))])
cv score: [0.63218391 0.63505747 0.52011494 0.61498708 0.44056848]
----------------------------------------
Trial 1433
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=32,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73706897 0.71408046 0.60201149 0.59173127 0.39664083]
----------------------------------------
Trial 1434
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.6))])
cv score: [0.67672414 0.62212644 0.64942529 0.62661499 0.4496124 ]
----------------------------------------
Trial 1435
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=195, random_state=100))])
cv score: [0.63793103 0.65948276 0.61637931 0.66925065 0.40310078]
----------------------------------------
Trial 1436
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=91,
random_state=100))])
cv score: [0.61494253 0.69109195 0.61781609 0.71963824 0.39018088]
----------------------------------------
Trial 1437
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.625 0.71408046 0.5704023 0.61498708 0.41472868]
----------------------------------------
Trial 1438
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73563218 0.62643678 0.68821839 0.6130491 0.42183463]
----------------------------------------
Trial 1439
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=169, random_state=100,
subsample=0.7))])
cv score: [0.64511494 0.69827586 0.49568966 0.67571059 0.45478036]
----------------------------------------
Trial 1440
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
n_estimators=154, random_state=100,
subsample=0.75))])
cv score: [0.55603448 0.73563218 0.62643678 0.71447028 0.45994832]
----------------------------------------
Trial 1441
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=21,
random_state=100))])
cv score: [0.59051724 0.68103448 0.61350575 0.59431525 0.32235142]
----------------------------------------
Trial 1442
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.68390805 0.59770115 0.60852713 0.40956072]
----------------------------------------
Trial 1443
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=97,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 1444
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=54, random_state=100,
subsample=0.85))])
cv score: [0.62068966 0.67385057 0.58477011 0.66149871 0.40697674]
----------------------------------------
Trial 1445
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56752874 0.73132184 0.58764368 0.48837209 0.40568475]
----------------------------------------
Trial 1446
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=190, random_state=100))])
cv score: [0.63649425 0.65804598 0.61350575 0.66925065 0.40180879]
----------------------------------------
Trial 1447
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=142,
random_state=100))])
cv score: [0.63649425 0.59841954 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 1448
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=199, random_state=100,
subsample=0.85))])
cv score: [0.68103448 0.68965517 0.63362069 0.65633075 0.49483204]
----------------------------------------
Trial 1449
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=141,
random_state=100))])
cv score: [0.66091954 0.64511494 0.65229885 0.64857881 0.4496124 ]
----------------------------------------
Trial 1450
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='log2',
n_estimators=89, random_state=100,
subsample=0.7))])
cv score: [0.61781609 0.66954023 0.54310345 0.72868217 0.5 ]
----------------------------------------
Trial 1451
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=90,
random_state=100))])
cv score: [0.5862069 0.60344828 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 1452
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=88, random_state=100))])
cv score: [0.61925287 0.68103448 0.60775862 0.67183463 0.42635659]
----------------------------------------
Trial 1453
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=103, random_state=100,
subsample=0.75))])
cv score: [0.66091954 0.47126437 0.43390805 0.49095607 0.46511628]
----------------------------------------
Trial 1454
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=40, random_state=100))])
cv score: [0.71408046 0.64367816 0.70689655 0.56589147 0.38113695]
----------------------------------------
Trial 1455
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=156,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.70545977 0.52873563 0.60723514 0.40697674]
----------------------------------------
Trial 1456
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=123, random_state=100))])
cv score: [0.75 0.63649425 0.66954023 0.57364341 0.40180879]
----------------------------------------
Trial 1457
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=97, random_state=100,
subsample=0.85))])
cv score: [0.65948276 0.57327586 0.59482759 0.62790698 0.40568475]
----------------------------------------
Trial 1458
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=117, random_state=100,
subsample=0.9))])
cv score: [0.60201149 0.65948276 0.54885057 0.61886305 0.4250646 ]
----------------------------------------
Trial 1459
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.67672414 0.62931034 0.58139535 0.44186047]
----------------------------------------
Trial 1460
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=12, random_state=100))])
cv score: [0.6875 0.67025862 0.47916667 0.66020672 0.32364341]
----------------------------------------
Trial 1461
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=178,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.69683908 0.54310345 0.54263566 0.40956072]
----------------------------------------
Trial 1462
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=127,
random_state=100))])
cv score: [0.76724138 0.63218391 0.66307471 0.63049096 0.3875969 ]
----------------------------------------
Trial 1463
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=18,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.66738506 0.59482759 0.65503876 0.47157623]
----------------------------------------
Trial 1464
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=66, random_state=100,
subsample=0.7))])
cv score: [0.51867816 0.68534483 0.54310345 0.61111111 0.53229974]
----------------------------------------
Trial 1465
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=124,
random_state=100))])
cv score: [0.55172414 0.65732759 0.59626437 0.73514212 0.43346253]
----------------------------------------
Trial 1466
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=149, random_state=100))])
cv score: [0.63936782 0.6637931 0.62643678 0.68217054 0.39793282]
----------------------------------------
Trial 1467
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=181, random_state=100))])
cv score: [0.61206897 0.66954023 0.58908046 0.62273902 0.44573643]
----------------------------------------
Trial 1468
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=189,
random_state=100))])
cv score: [0.63793103 0.66954023 0.5933908 0.63824289 0.42635659]
----------------------------------------
Trial 1469
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=79,
random_state=100))])
cv score: [0.72557471 0.64942529 0.64367816 0.60465116 0.37984496]
----------------------------------------
Trial 1470
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=92,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.60344828 0.56609195 0.5878553 0.38372093]
----------------------------------------
Trial 1471
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=33,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72270115 0.68534483 0.69683908 0.64470284 0.44573643]
----------------------------------------
Trial 1472
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.7))])
cv score: [0.68678161 0.48922414 0.28591954 0.34496124 0.50581395]
----------------------------------------
Trial 1473
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='sqrt',
n_estimators=69, random_state=100,
subsample=0.6))])
cv score: [0.7183908 0.65229885 0.53591954 0.64082687 0.45865633]
----------------------------------------
Trial 1474
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
n_estimators=58, random_state=100,
subsample=0.65))])
cv score: [0.67816092 0.62931034 0.58333333 0.63824289 0.44444444]
----------------------------------------
Trial 1475
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=90, random_state=100,
subsample=0.65))])
cv score: [0.60057471 0.64367816 0.54741379 0.72351421 0.50387597]
----------------------------------------
Trial 1476
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=182,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.6637931 0.65086207 0.61757106 0.41860465]
----------------------------------------
Trial 1477
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=170, random_state=100,
subsample=0.65))])
cv score: [0.73706897 0.67816092 0.6566092 0.6744186 0.51033592]
----------------------------------------
Trial 1478
----------------------------------------
Parameters {'xgb__n_estimators': 99, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=99,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5933908 0.64655172 0.55890805 0.51421189 0.44315245]
----------------------------------------
Trial 1479
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=123,
random_state=100))])
cv score: [0.59482759 0.69396552 0.48850575 0.56072351 0.44444444]
----------------------------------------
Trial 1480
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
n_estimators=105, random_state=100,
subsample=0.95))])
cv score: [0.54597701 0.68103448 0.60201149 0.6369509 0.42183463]
----------------------------------------
Trial 1481
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=145,
random_state=100))])
cv score: [0.54741379 0.65876437 0.59913793 0.74677003 0.41795866]
----------------------------------------
Trial 1482
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='sqrt',
n_estimators=112, random_state=100,
subsample=0.75))])
cv score: [0.5862069 0.62787356 0.44109195 0.64082687 0.49741602]
----------------------------------------
Trial 1483
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=37, random_state=100,
subsample=0.9))])
cv score: [0.67816092 0.64511494 0.44971264 0.63307494 0.46124031]
----------------------------------------
Trial 1484
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=122, random_state=100))])
cv score: [0.60344828 0.71048851 0.63074713 0.66537468 0.41989664]
----------------------------------------
Trial 1485
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70402299 0.66235632 0.35847701 0.59237726 0.37338501]
----------------------------------------
Trial 1486
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=124, random_state=100))])
cv score: [0.76293103 0.63362069 0.64511494 0.6369509 0.42118863]
----------------------------------------
Trial 1487
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=71,
random_state=100))])
cv score: [0.64367816 0.68534483 0.55747126 0.59173127 0.44315245]
----------------------------------------
Trial 1488
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=193,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.70258621 0.61925287 0.59302326 0.42248062]
----------------------------------------
Trial 1489
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=129,
random_state=100))])
cv score: [0.55603448 0.65732759 0.58908046 0.7377261 0.42571059]
----------------------------------------
Trial 1490
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=72,
random_state=100))])
cv score: [0.69683908 0.63362069 0.6637931 0.65374677 0.374677 ]
----------------------------------------
Trial 1491
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=96,
random_state=100))])
cv score: [0.64798851 0.63936782 0.61925287 0.63436693 0.42894057]
----------------------------------------
Trial 1492
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=96, random_state=100))])
cv score: [0.75143678 0.63505747 0.6566092 0.625323 0.40310078]
----------------------------------------
Trial 1493
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=127, random_state=100,
subsample=0.65))])
cv score: [0.48060345 0.50574713 0.36925287 0.36950904 0.55167959]
----------------------------------------
Trial 1494
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=59, random_state=100,
subsample=0.6))])
cv score: [0.65517241 0.61781609 0.44827586 0.64341085 0.41989664]
----------------------------------------
Trial 1495
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=40,
random_state=100))])
cv score: [0.6408046 0.60201149 0.5862069 0.56718346 0.37726098]
----------------------------------------
Trial 1496
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=130, random_state=100,
subsample=0.85))])
cv score: [0.6637931 0.67816092 0.5545977 0.70155039 0.46511628]
----------------------------------------
Trial 1497
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.61781609 0.68247126 0.62919897 0.38888889]
----------------------------------------
Trial 1498
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=122,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72341954 0.60416667 0.64439655 0.62273902 0.43604651]
----------------------------------------
Trial 1499
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=21, random_state=100))])
cv score: [0.69396552 0.68031609 0.5862069 0.62273902 0.3875969 ]
----------------------------------------
Trial 1500
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=168,
random_state=100))])
cv score: [0.65373563 0.64224138 0.61781609 0.65116279 0.44702842]
----------------------------------------
Trial 1501
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=194, random_state=100))])
cv score: [0.66810345 0.63505747 0.59770115 0.6124031 0.40180879]
----------------------------------------
Trial 1502
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=75,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5704023 0.69971264 0.5545977 0.60852713 0.44056848]
----------------------------------------
Trial 1503
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=24, random_state=100,
subsample=0.8))])
cv score: [0.64655172 0.66091954 0.54022989 0.62015504 0.45865633]
----------------------------------------
Trial 1504
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=173, random_state=100,
subsample=0.8))])
cv score: [0.64367816 0.6795977 0.50287356 0.58656331 0.49870801]
----------------------------------------
Trial 1505
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=69,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68390805 0.67672414 0.61350575 0.60723514 0.47932817]
----------------------------------------
Trial 1506
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=165, random_state=100,
subsample=0.85))])
cv score: [0.59051724 0.67672414 0.54166667 0.63436693 0.48320413]
----------------------------------------
Trial 1507
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75933908 0.56178161 0.51293103 0.52648579 0.37790698]
----------------------------------------
Trial 1508
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70977011 0.6637931 0.63793103 0.6375969 0.41085271]
----------------------------------------
Trial 1509
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
n_estimators=97, random_state=100,
subsample=0.6))])
cv score: [0.56896552 0.72270115 0.6091954 0.69121447 0.45348837]
----------------------------------------
Trial 1510
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=96,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.64367816 0.66235632 0.60852713 0.43023256]
----------------------------------------
Trial 1511
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=164,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.63649425 0.61063218 0.61498708 0.42377261]
----------------------------------------
Trial 1512
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=45, random_state=100))])
cv score: [0.54238506 0.67887931 0.5783046 0.59560724 0.48126615]
----------------------------------------
Trial 1513
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=124,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.74712644 0.55316092 0.6124031 0.45413437]
----------------------------------------
Trial 1514
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=55,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5316092 0.71695402 0.56178161 0.58397933 0.50516796]
----------------------------------------
Trial 1515
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=136, random_state=100,
subsample=0.65))])
cv score: [0.75287356 0.67528736 0.66235632 0.66537468 0.49354005]
----------------------------------------
Trial 1516
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=18,
random_state=100))])
cv score: [0.71982759 0.63362069 0.45114943 0.60142119 0.35917313]
----------------------------------------
Trial 1517
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=106, random_state=100,
subsample=0.85))])
cv score: [0.67528736 0.70833333 0.63074713 0.63953488 0.52583979]
----------------------------------------
Trial 1518
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='sqrt',
n_estimators=165, random_state=100,
subsample=0.8))])
cv score: [0.35632184 0.60057471 0.50862069 0.62144703 0.53100775]
----------------------------------------
Trial 1519
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69755747 0.69971264 0.72485632 0.625323 0.38630491]
----------------------------------------
Trial 1520
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.60201149 0.47126437 0.52002584 0.37015504]
----------------------------------------
Trial 1521
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=109, random_state=100,
subsample=0.6))])
cv score: [0.62356322 0.67528736 0.48994253 0.61111111 0.45478036]
----------------------------------------
Trial 1522
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=152,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69252874 0.67528736 0.625 0.63824289 0.43540052]
----------------------------------------
Trial 1523
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69396552 0.65948276 0.65086207 0.60594315 0.39793282]
----------------------------------------
Trial 1524
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=83,
random_state=100))])
cv score: [0.60201149 0.66882184 0.6170977 0.67700258 0.4005168 ]
----------------------------------------
Trial 1525
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.72988506 0.57758621 0.5878553 0.43927649]
----------------------------------------
Trial 1526
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=105,
random_state=100))])
cv score: [0.60057471 0.66091954 0.60632184 0.65891473 0.4121447 ]
----------------------------------------
Trial 1527
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=39, random_state=100,
subsample=0.95))])
cv score: [0.6566092 0.72054598 0.60057471 0.49224806 0.34560724]
----------------------------------------
Trial 1528
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54885057 0.70833333 0.52011494 0.5503876 0.36950904]
----------------------------------------
Trial 1529
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=105, random_state=100,
subsample=0.85))])
cv score: [0.625 0.74137931 0.42385057 0.624677 0.46124031]
----------------------------------------
Trial 1530
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.4841954 0.47126437 0.52260982 0.41860465]
----------------------------------------
Trial 1531
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=110, random_state=100,
subsample=0.65))])
cv score: [0.65948276 0.51724138 0.62140805 0.7118863 0.4870801 ]
----------------------------------------
Trial 1532
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=35, random_state=100))])
cv score: [0.57543103 0.70545977 0.64655172 0.58656331 0.48126615]
----------------------------------------
Trial 1533
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57758621 0.75431034 0.63649425 0.60723514 0.38113695]
----------------------------------------
Trial 1534
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=68,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75 0.5704023 0.68606322 0.61627907 0.375323 ]
----------------------------------------
Trial 1535
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=156, random_state=100,
subsample=0.65))])
cv score: [0.60201149 0.63793103 0.54741379 0.5994832 0.46124031]
----------------------------------------
Trial 1536
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
n_estimators=135, random_state=100,
subsample=0.6))])
cv score: [0.60632184 0.70833333 0.63649425 0.69509044 0.45090439]
----------------------------------------
Trial 1537
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=122, random_state=100))])
cv score: [0.61637931 0.69683908 0.66666667 0.67571059 0.42700258]
----------------------------------------
Trial 1538
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=191,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.71408046 0.69109195 0.63565891 0.3875969 ]
----------------------------------------
Trial 1539
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=174, random_state=100,
subsample=0.85))])
cv score: [0.49568966 0.58764368 0.4683908 0.63565891 0.44702842]
----------------------------------------
Trial 1540
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.61206897 0.5545977 0.62403101 0.36434109]
----------------------------------------
Trial 1541
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=124,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.63793103 0.61637931 0.62273902 0.40956072]
----------------------------------------
Trial 1542
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=83, random_state=100))])
cv score: [0.73275862 0.62068966 0.66522989 0.5620155 0.38372093]
----------------------------------------
Trial 1543
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=89, random_state=100,
subsample=0.75))])
cv score: [0.54885057 0.62212644 0.62212644 0.66537468 0.45478036]
----------------------------------------
Trial 1544
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=160,
random_state=100, subsample=0.8))])
cv score: [0.56178161 0.70114943 0.59913793 0.625323 0.44186047]
----------------------------------------
Trial 1545
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=84,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56034483 0.71408046 0.52298851 0.55297158 0.44056848]
----------------------------------------
Trial 1546
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
random_state=100))])
cv score: [0.60488506 0.67816092 0.59482759 0.63178295 0.42377261]
----------------------------------------
Trial 1547
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
n_estimators=48, random_state=100,
subsample=0.85))])
cv score: [0.57327586 0.77011494 0.67097701 0.72739018 0.38113695]
----------------------------------------
Trial 1548
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=148, random_state=100,
subsample=0.7))])
cv score: [0.57902299 0.58189655 0.56465517 0.74160207 0.55426357]
----------------------------------------
Trial 1549
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
n_estimators=50, random_state=100,
subsample=0.65))])
cv score: [0.66666667 0.74425287 0.51293103 0.66666667 0.50129199]
----------------------------------------
Trial 1550
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65948276 0.6408046 0.75 0.68927649 0.39857881]
----------------------------------------
Trial 1551
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=59, random_state=100,
subsample=0.7))])
cv score: [0.6954023 0.63793103 0.63362069 0.6744186 0.41343669]
----------------------------------------
Trial 1552
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=143, random_state=100,
subsample=0.6))])
cv score: [0.60488506 0.46408046 0.49856322 0.66020672 0.32170543]
----------------------------------------
Trial 1553
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=140, random_state=100,
subsample=0.75))])
cv score: [0.6637931 0.62931034 0.49712644 0.64470284 0.4625323 ]
----------------------------------------
Trial 1554
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=101,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76867816 0.57758621 0.7420977 0.64470284 0.41731266]
----------------------------------------
Trial 1555
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=50,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.66810345 0.625 0.62403101 0.39534884]
----------------------------------------
Trial 1556
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=148,
random_state=100))])
cv score: [0.72557471 0.64511494 0.58045977 0.62919897 0.42248062]
----------------------------------------
Trial 1557
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
n_estimators=193, random_state=100,
subsample=0.85))])
cv score: [0.55316092 0.74137931 0.63074713 0.70413437 0.39534884]
----------------------------------------
Trial 1558
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='log2',
n_estimators=133, random_state=100,
subsample=0.7))])
cv score: [0.73994253 0.66954023 0.58189655 0.64341085 0.52067183]
----------------------------------------
Trial 1559
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=183, random_state=100,
subsample=0.9))])
cv score: [0.55172414 0.66235632 0.63793103 0.64857881 0.45348837]
----------------------------------------
Trial 1560
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='log2',
n_estimators=175, random_state=100,
subsample=0.9))])
cv score: [0.60488506 0.6954023 0.49281609 0.63565891 0.54005168]
----------------------------------------
Trial 1561
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=162,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.69827586 0.58477011 0.56330749 0.44315245]
----------------------------------------
Trial 1562
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76939655 0.64295977 0.65014368 0.55878553 0.36821705]
----------------------------------------
Trial 1563
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=106,
random_state=100))])
cv score: [0.77873563 0.63074713 0.66666667 0.63436693 0.4005168 ]
----------------------------------------
Trial 1564
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=197, random_state=100))])
cv score: [0.57758621 0.66810345 0.61566092 0.65503876 0.42764858]
----------------------------------------
Trial 1565
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=187, random_state=100,
subsample=0.6))])
cv score: [0.61494253 0.52586207 0.21408046 0.48320413 0.4870801 ]
----------------------------------------
Trial 1566
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
n_estimators=27, random_state=100,
subsample=0.8))])
cv score: [0.6408046 0.75143678 0.61781609 0.74418605 0.39018088]
----------------------------------------
Trial 1567
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=56, random_state=100))])
cv score: [0.61350575 0.70402299 0.64798851 0.7002584 0.43410853]
----------------------------------------
Trial 1568
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=20,
random_state=100))])
cv score: [0.6795977 0.61206897 0.56321839 0.6744186 0.40439276]
----------------------------------------
Trial 1569
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=34,
random_state=100))])
cv score: [0.77586207 0.63936782 0.64008621 0.62144703 0.42571059]
----------------------------------------
Trial 1570
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.56465517 0.63218391 0.71834625 0.43087855]
----------------------------------------
Trial 1571
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=146,
random_state=100))])
cv score: [0.61063218 0.63649425 0.59195402 0.62790698 0.45607235]
----------------------------------------
Trial 1572
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=99, random_state=100))])
cv score: [0.54454023 0.66738506 0.6012931 0.57881137 0.40633075]
----------------------------------------
Trial 1573
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64367816 0.58045977 0.49568966 0.52971576 0.42635659]
----------------------------------------
Trial 1574
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=62, random_state=100,
subsample=0.75))])
cv score: [0.59195402 0.68965517 0.50862069 0.55943152 0.51808786]
----------------------------------------
Trial 1575
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='sqrt',
n_estimators=109, random_state=100,
subsample=0.65))])
cv score: [0.65373563 0.6795977 0.49281609 0.64728682 0.49483204]
----------------------------------------
Trial 1576
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='sqrt',
n_estimators=20, random_state=100,
subsample=0.8))])
cv score: [0.56034483 0.46695402 0.55316092 0.59173127 0.48837209]
----------------------------------------
Trial 1577
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=73, random_state=100))])
cv score: [0.64511494 0.60057471 0.55603448 0.59173127 0.44444444]
----------------------------------------
Trial 1578
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=12, random_state=100,
subsample=0.75))])
cv score: [0.46408046 0.63793103 0.51867816 0.65633075 0.37596899]
----------------------------------------
Trial 1579
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='sqrt',
n_estimators=32, random_state=100,
subsample=0.6))])
cv score: [0.52155172 0.56609195 0.60057471 0.6744186 0.47157623]
----------------------------------------
Trial 1580
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=108, random_state=100,
subsample=0.9))])
cv score: [0.625 0.7112069 0.56178161 0.67571059 0.47028424]
----------------------------------------
Trial 1581
----------------------------------------
Parameters {'xgb__n_estimators': 18, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=18,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.57758621 0.47126437 0.52713178 0.41860465]
----------------------------------------
Trial 1582
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=57, random_state=100))])
cv score: [0.72701149 0.67097701 0.61925287 0.66537468 0.48320413]
----------------------------------------
Trial 1583
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='sqrt',
n_estimators=148, random_state=100,
subsample=0.8))])
cv score: [0.64367816 0.69827586 0.61350575 0.56718346 0.51937984]
----------------------------------------
Trial 1584
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=65, random_state=100))])
cv score: [0.6954023 0.65948276 0.57471264 0.53488372 0.36563307]
----------------------------------------
Trial 1585
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=135,
random_state=100))])
cv score: [0.74712644 0.64798851 0.68965517 0.64728682 0.43023256]
----------------------------------------
Trial 1586
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.62212644 0.66091954 0.61627907 0.4121447 ]
----------------------------------------
Trial 1587
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='log2',
n_estimators=123, random_state=100,
subsample=0.6))])
cv score: [0.59195402 0.70258621 0.625 0.68346253 0.40826873]
----------------------------------------
Trial 1588
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=182, random_state=100))])
cv score: [0.71982759 0.65948276 0.64655172 0.67054264 0.4379845 ]
----------------------------------------
Trial 1589
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=199,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.67528736 0.5545977 0.57751938 0.44056848]
----------------------------------------
Trial 1590
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=143, random_state=100))])
cv score: [0.62787356 0.6408046 0.64511494 0.59043928 0.40956072]
----------------------------------------
Trial 1591
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=189,
random_state=100))])
cv score: [0.54741379 0.65517241 0.59195402 0.73643411 0.40310078]
----------------------------------------
Trial 1592
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='log2',
n_estimators=178, random_state=100,
subsample=0.6))])
cv score: [0.60488506 0.68534483 0.50862069 0.70671835 0.62919897]
----------------------------------------
Trial 1593
----------------------------------------
Parameters {'gb__n_estimators': 55, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=55, random_state=100,
subsample=0.9))])
cv score: [0.55028736 0.63793103 0.45258621 0.62661499 0.41472868]
----------------------------------------
Trial 1594
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=122, random_state=100))])
cv score: [0.56465517 0.66235632 0.58477011 0.625323 0.43927649]
----------------------------------------
Trial 1595
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=150, random_state=100,
subsample=0.75))])
cv score: [0.68678161 0.66666667 0.53735632 0.57751938 0.43927649]
----------------------------------------
Trial 1596
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=81, random_state=100))])
cv score: [0.72557471 0.61206897 0.55172414 0.57364341 0.42118863]
----------------------------------------
Trial 1597
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=156, random_state=100))])
cv score: [0.60344828 0.73275862 0.64655172 0.69444444 0.40891473]
----------------------------------------
Trial 1598
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=148, random_state=100))])
cv score: [0.61781609 0.70258621 0.6795977 0.67571059 0.42700258]
----------------------------------------
Trial 1599
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=192, random_state=100,
subsample=0.6))])
cv score: [0.58908046 0.7341954 0.51005747 0.6124031 0.50775194]
----------------------------------------
Trial 1600
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=153, random_state=100))])
cv score: [0.71695402 0.63649425 0.6637931 0.57364341 0.41085271]
----------------------------------------
Trial 1601
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=34, random_state=100))])
cv score: [0.72126437 0.65948276 0.67816092 0.52390181 0.39405685]
----------------------------------------
Trial 1602
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.65948276 0.68678161 0.65633075 0.41085271]
----------------------------------------
Trial 1603
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=90, random_state=100,
subsample=0.8))])
cv score: [0.58189655 0.69109195 0.5158046 0.58268734 0.40439276]
----------------------------------------
Trial 1604
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=39,
random_state=100))])
cv score: [0.77586207 0.6408046 0.63146552 0.66795866 0.41408269]
----------------------------------------
Trial 1605
----------------------------------------
Parameters {'rf__n_estimators': 25, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=25,
random_state=100))])
cv score: [0.63649425 0.58477011 0.57183908 0.60723514 0.39793282]
----------------------------------------
Trial 1606
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=13,
random_state=100))])
cv score: [0.63793103 0.6408046 0.60632184 0.50516796 0.45219638]
----------------------------------------
Trial 1607
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
n_estimators=101, random_state=100,
subsample=0.75))])
cv score: [0.50287356 0.70402299 0.61494253 0.71059432 0.45994832]
----------------------------------------
Trial 1608
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=175, random_state=100))])
cv score: [0.57614943 0.64224138 0.57758621 0.64341085 0.42377261]
----------------------------------------
Trial 1609
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=152, random_state=100))])
cv score: [0.59698276 0.72701149 0.6487069 0.68152455 0.41860465]
----------------------------------------
Trial 1610
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=198,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.69252874 0.63936782 0.54392765 0.40310078]
----------------------------------------
Trial 1611
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=69, random_state=100,
subsample=0.7))])
cv score: [0.75287356 0.64367816 0.64367816 0.66795866 0.40956072]
----------------------------------------
Trial 1612
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=62, random_state=100,
subsample=0.75))])
cv score: [0.65948276 0.62787356 0.5 0.66795866 0.39018088]
----------------------------------------
Trial 1613
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='log2',
n_estimators=129, random_state=100,
subsample=0.95))])
cv score: [0.61925287 0.61925287 0.57183908 0.67700258 0.44056848]
----------------------------------------
Trial 1614
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68821839 0.67313218 0.64511494 0.65762274 0.40116279]
----------------------------------------
Trial 1615
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=82, random_state=100,
subsample=0.75))])
cv score: [0.61494253 0.73994253 0.51149425 0.60981912 0.44573643]
----------------------------------------
Trial 1616
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75646552 0.58764368 0.67313218 0.55749354 0.43927649]
----------------------------------------
Trial 1617
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=43,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.6566092 0.60632184 0.57364341 0.3875969 ]
----------------------------------------
Trial 1618
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=181,
random_state=100))])
cv score: [0.61925287 0.59841954 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 1619
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=197, random_state=100))])
cv score: [0.61781609 0.67385057 0.57327586 0.62144703 0.43927649]
----------------------------------------
Trial 1620
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
n_estimators=144, random_state=100,
subsample=0.9))])
cv score: [0.53591954 0.73994253 0.54022989 0.69638243 0.39534884]
----------------------------------------
Trial 1621
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70114943 0.5862069 0.62356322 0.54263566 0.41343669]
----------------------------------------
Trial 1622
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=105, random_state=100))])
cv score: [0.70258621 0.67528736 0.61063218 0.65503876 0.45090439]
----------------------------------------
Trial 1623
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=39,
random_state=100))])
cv score: [0.60057471 0.66091954 0.65229885 0.64599483 0.38888889]
----------------------------------------
Trial 1624
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=101, random_state=100))])
cv score: [0.57112069 0.69683908 0.6091954 0.59625323 0.44509044]
----------------------------------------
Trial 1625
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=180, random_state=100))])
cv score: [0.74568966 0.63074713 0.66235632 0.66149871 0.42248062]
----------------------------------------
Trial 1626
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=199,
random_state=100))])
cv score: [0.63864943 0.59985632 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 1627
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=176, random_state=100,
subsample=0.8))])
cv score: [0.70258621 0.66666667 0.61781609 0.64341085 0.46899225]
----------------------------------------
Trial 1628
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=176, random_state=100))])
cv score: [0.60344828 0.67097701 0.58045977 0.63049096 0.43540052]
----------------------------------------
Trial 1629
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80531609 0.58477011 0.53663793 0.5503876 0.39793282]
----------------------------------------
Trial 1630
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=75,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.70977011 0.64367816 0.61369509 0.42248062]
----------------------------------------
Trial 1631
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='log2',
n_estimators=114, random_state=100,
subsample=0.6))])
cv score: [0.59195402 0.5545977 0.57614943 0.72868217 0.35658915]
----------------------------------------
Trial 1632
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='sqrt',
n_estimators=21, random_state=100,
subsample=0.6))])
cv score: [0.50862069 0.62643678 0.4841954 0.70284238 0.5749354 ]
----------------------------------------
Trial 1633
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.70114943 0.68175287 0.66149871 0.42764858]
----------------------------------------
Trial 1634
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=114,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72270115 0.67385057 0.67672414 0.5878553 0.48449612]
----------------------------------------
Trial 1635
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=158, random_state=100))])
cv score: [0.59913793 0.70617816 0.66954023 0.70671835 0.42635659]
----------------------------------------
Trial 1636
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.64655172 0.67816092 0.66666667 0.41343669]
----------------------------------------
Trial 1637
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=191, random_state=100,
subsample=0.7))])
cv score: [0.57327586 0.62212644 0.52442529 0.64341085 0.43927649]
----------------------------------------
Trial 1638
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=48,
random_state=100))])
cv score: [0.68390805 0.62931034 0.65948276 0.60852713 0.37209302]
----------------------------------------
Trial 1639
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=101, random_state=100,
subsample=0.65))])
cv score: [0.54166667 0.72701149 0.5933908 0.68346253 0.4121447 ]
----------------------------------------
Trial 1640
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76077586 0.62212644 0.68103448 0.62273902 0.47157623]
----------------------------------------
Trial 1641
----------------------------------------
Parameters {'xgb__n_estimators': 160, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=160,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56824713 0.67241379 0.74425287 0.61563307 0.38049096]
----------------------------------------
Trial 1642
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=42, random_state=100,
subsample=0.6))])
cv score: [0.52011494 0.42816092 0.6566092 0.53617571 0.39793282]
----------------------------------------
Trial 1643
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=33,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57614943 0.64655172 0.52586207 0.50645995 0.38630491]
----------------------------------------
Trial 1644
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=188, random_state=100))])
cv score: [0.72557471 0.6408046 0.63936782 0.6124031 0.40439276]
----------------------------------------
Trial 1645
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.68103448 0.61063218 0.43023256 0.39018088]
----------------------------------------
Trial 1646
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=177,
random_state=100))])
cv score: [0.58979885 0.60201149 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 1647
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=181, random_state=100))])
cv score: [0.59770115 0.72772989 0.65086207 0.67312661 0.43281654]
----------------------------------------
Trial 1648
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=37,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5933908 0.70114943 0.6637931 0.59431525 0.40180879]
----------------------------------------
Trial 1649
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=194,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79813218 0.67241379 0.70402299 0.61111111 0.4121447 ]
----------------------------------------
Trial 1650
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=99,
random_state=100))])
cv score: [0.60057471 0.68821839 0.61494253 0.68346253 0.40891473]
----------------------------------------
Trial 1651
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=25,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.59626437 0.61781609 0.63953488 0.4625323 ]
----------------------------------------
Trial 1652
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=189, random_state=100,
subsample=0.6))])
cv score: [0.68678161 0.70833333 0.56896552 0.65633075 0.41343669]
----------------------------------------
Trial 1653
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=87,
random_state=100))])
cv score: [0.62068966 0.64367816 0.58764368 0.64599483 0.40180879]
----------------------------------------
Trial 1654
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=10, random_state=100,
subsample=0.95))])
cv score: [0.6408046 0.64439655 0.60057471 0.48449612 0.44509044]
----------------------------------------
Trial 1655
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.71551724 0.5387931 0.56847545 0.43152455]
----------------------------------------
Trial 1656
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79382184 0.61494253 0.71408046 0.63824289 0.4496124 ]
----------------------------------------
Trial 1657
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=153,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 1658
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=57,
random_state=100))])
cv score: [0.62787356 0.68965517 0.62212644 0.58656331 0.4005168 ]
----------------------------------------
Trial 1659
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=163,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.68821839 0.64655172 0.5374677 0.46576227]
----------------------------------------
Trial 1660
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=20, random_state=100,
subsample=0.8))])
cv score: [0.63649425 0.60057471 0.59770115 0.62015504 0.43023256]
----------------------------------------
Trial 1661
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=165, random_state=100))])
cv score: [0.63793103 0.66091954 0.60775862 0.61627907 0.45736434]
----------------------------------------
Trial 1662
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.65373563 0.57758621 0.52196382 0.4496124 ]
----------------------------------------
Trial 1663
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=189,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74353448 0.68390805 0.71551724 0.62984496 0.42700258]
----------------------------------------
Trial 1664
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=76, random_state=100))])
cv score: [0.74281609 0.66091954 0.66522989 0.63049096 0.45994832]
----------------------------------------
Trial 1665
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=56, random_state=100,
subsample=0.85))])
cv score: [0.56034483 0.72701149 0.58189655 0.65245478 0.43152455]
----------------------------------------
Trial 1666
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=36, random_state=100,
subsample=0.6))])
cv score: [0.61566092 0.50359195 0.45114943 0.62984496 0.4121447 ]
----------------------------------------
Trial 1667
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=50, random_state=100,
subsample=0.6))])
cv score: [0.63218391 0.68390805 0.42241379 0.62919897 0.44702842]
----------------------------------------
Trial 1668
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=47, random_state=100,
subsample=0.85))])
cv score: [0.65804598 0.63074713 0.48563218 0.6124031 0.38501292]
----------------------------------------
Trial 1669
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=63, random_state=100,
subsample=0.9))])
cv score: [0.61494253 0.68534483 0.52155172 0.65116279 0.36563307]
----------------------------------------
Trial 1670
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=32, random_state=100,
subsample=0.75))])
cv score: [0.65517241 0.5545977 0.41954023 0.5994832 0.51679587]
----------------------------------------
Trial 1671
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=22,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72198276 0.63936782 0.59195402 0.67700258 0.4748062 ]
----------------------------------------
Trial 1672
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=167, random_state=100))])
cv score: [0.59913793 0.72485632 0.64798851 0.68475452 0.43217054]
----------------------------------------
Trial 1673
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=90,
random_state=100))])
cv score: [0.72270115 0.64655172 0.64224138 0.60852713 0.37209302]
----------------------------------------
Trial 1674
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=109, random_state=100))])
cv score: [0.61206897 0.67385057 0.60488506 0.63824289 0.4379845 ]
----------------------------------------
Trial 1675
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=148,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77155172 0.6408046 0.69683908 0.58979328 0.36627907]
----------------------------------------
Trial 1676
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='sqrt',
n_estimators=39, random_state=100,
subsample=0.85))])
cv score: [0.68821839 0.62643678 0.69109195 0.63953488 0.38501292]
----------------------------------------
Trial 1677
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.75))])
cv score: [0.68390805 0.66666667 0.53591954 0.57622739 0.44315245]
----------------------------------------
Trial 1678
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=158,
random_state=100))])
cv score: [0.77586207 0.64224138 0.68965517 0.6369509 0.39922481]
----------------------------------------
Trial 1679
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=136, random_state=100,
subsample=0.6))])
cv score: [0.66810345 0.60201149 0.52155172 0.57235142 0.44056848]
----------------------------------------
Trial 1680
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=167,
random_state=100))])
cv score: [0.77298851 0.62356322 0.68606322 0.65503876 0.40697674]
----------------------------------------
Trial 1681
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=12,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75287356 0.55316092 0.57255747 0.53617571 0.46576227]
----------------------------------------
Trial 1682
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
n_estimators=151, random_state=100,
subsample=0.75))])
cv score: [0.57327586 0.69396552 0.53448276 0.70284238 0.45478036]
----------------------------------------
Trial 1683
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=92,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76652299 0.58764368 0.73994253 0.66925065 0.43346253]
----------------------------------------
Trial 1684
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=43, random_state=100))])
cv score: [0.61278736 0.70617816 0.6408046 0.69509044 0.45478036]
----------------------------------------
Trial 1685
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='log2',
n_estimators=21, random_state=100,
subsample=0.85))])
cv score: [0.77658046 0.60344828 0.7579023 0.64728682 0.37144703]
----------------------------------------
Trial 1686
----------------------------------------
Parameters {'gb__n_estimators': 55, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=55, random_state=100,
subsample=0.95))])
cv score: [0.6091954 0.67672414 0.70258621 0.71447028 0.39147287]
----------------------------------------
Trial 1687
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=52, random_state=100))])
cv score: [0.70977011 0.66954023 0.64655172 0.62015504 0.34625323]
----------------------------------------
Trial 1688
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=69, random_state=100))])
cv score: [0.63793103 0.60201149 0.57327586 0.5878553 0.43927649]
----------------------------------------
Trial 1689
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=189,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65229885 0.57255747 0.68103448 0.55684755 0.36434109]
----------------------------------------
Trial 1690
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=70,
random_state=100))])
cv score: [0.60488506 0.6329023 0.60201149 0.69379845 0.37984496]
----------------------------------------
Trial 1691
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=177,
random_state=100))])
cv score: [0.62356322 0.65517241 0.60344828 0.67700258 0.46511628]
----------------------------------------
Trial 1692
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=112,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 1693
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=178, random_state=100))])
cv score: [0.65948276 0.67528736 0.60057471 0.67958656 0.43669251]
----------------------------------------
Trial 1694
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.67097701 0.70114943 0.59043928 0.39147287]
----------------------------------------
Trial 1695
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='log2',
n_estimators=184, random_state=100,
subsample=0.6))])
cv score: [0.5933908 0.4533046 0.46192529 0.4870801 0.58850129]
----------------------------------------
Trial 1696
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=34, random_state=100))])
cv score: [0.67816092 0.69396552 0.5933908 0.625323 0.39405685]
----------------------------------------
Trial 1697
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=182,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56178161 0.64224138 0.63793103 0.5878553 0.4625323 ]
----------------------------------------
Trial 1698
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77945402 0.62787356 0.63649425 0.61627907 0.45801034]
----------------------------------------
Trial 1699
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=60,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55747126 0.6408046 0.44683908 0.57881137 0.4754522 ]
----------------------------------------
Trial 1700
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7341954 0.65229885 0.7262931 0.60594315 0.46317829]
----------------------------------------
Trial 1701
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=149, random_state=100,
subsample=0.65))])
cv score: [0.57758621 0.7341954 0.61350575 0.66666667 0.43669251]
----------------------------------------
Trial 1702
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=97, random_state=100,
subsample=0.8))])
cv score: [0.57614943 0.75 0.54166667 0.69767442 0.40826873]
----------------------------------------
Trial 1703
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=127, random_state=100,
subsample=0.75))])
cv score: [0.57471264 0.64798851 0.50574713 0.64082687 0.49612403]
----------------------------------------
Trial 1704
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=180,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66666667 0.64511494 0.68390805 0.58397933 0.41989664]
----------------------------------------
Trial 1705
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=144,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 1706
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=86,
random_state=100))])
cv score: [0.71982759 0.64511494 0.625 0.60335917 0.38113695]
----------------------------------------
Trial 1707
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=16, random_state=100))])
cv score: [0.47772989 0.625 0.60847701 0.5626615 0.50387597]
----------------------------------------
Trial 1708
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=65,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 1709
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=42,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.72844828 0.76293103 0.64276486 0.3998708 ]
----------------------------------------
Trial 1710
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=186,
random_state=100))])
cv score: [0.63362069 0.66666667 0.57758621 0.63953488 0.42764858]
----------------------------------------
Trial 1711
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6795977 0.70402299 0.65373563 0.5129199 0.42635659]
----------------------------------------
Trial 1712
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=133, random_state=100,
subsample=0.85))])
cv score: [0.65373563 0.67385057 0.47701149 0.63565891 0.42118863]
----------------------------------------
Trial 1713
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
n_estimators=109, random_state=100,
subsample=0.7))])
cv score: [0.64942529 0.69252874 0.50862069 0.72997416 0.48966408]
----------------------------------------
Trial 1714
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=65, random_state=100,
subsample=0.7))])
cv score: [0.54741379 0.60201149 0.60632184 0.74289406 0.39664083]
----------------------------------------
Trial 1715
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64367816 0.63505747 0.55316092 0.56589147 0.40826873]
----------------------------------------
Trial 1716
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=172, random_state=100,
subsample=0.6))])
cv score: [0.62787356 0.6954023 0.51293103 0.67700258 0.45607235]
----------------------------------------
Trial 1717
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=72,
random_state=100))])
cv score: [0.69612069 0.63505747 0.51724138 0.5381137 0.36950904]
----------------------------------------
Trial 1718
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68318966 0.68821839 0.64942529 0.66537468 0.41085271]
----------------------------------------
Trial 1719
----------------------------------------
Parameters {'xgb__n_estimators': 171, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=171,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.71551724 0.69683908 0.65116279 0.41731266]
----------------------------------------
Trial 1720
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=193, random_state=100,
subsample=0.7))])
cv score: [0.59051724 0.70833333 0.60057471 0.61627907 0.43023256]
----------------------------------------
Trial 1721
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=84,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75 0.55890805 0.72270115 0.65826873 0.39276486]
----------------------------------------
Trial 1722
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73778736 0.58189655 0.72054598 0.55167959 0.36434109]
----------------------------------------
Trial 1723
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=108, random_state=100,
subsample=0.85))])
cv score: [0.66666667 0.56465517 0.59913793 0.62015504 0.40568475]
----------------------------------------
Trial 1724
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=173,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 1725
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=87, random_state=100))])
cv score: [0.73132184 0.61063218 0.54741379 0.56718346 0.41602067]
----------------------------------------
Trial 1726
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=147, random_state=100,
subsample=0.6))])
cv score: [0.66235632 0.65804598 0.58477011 0.63436693 0.41989664]
----------------------------------------
Trial 1727
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.7112069 0.56178161 0.62273902 0.42248062]
----------------------------------------
Trial 1728
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=96, random_state=100))])
cv score: [0.64511494 0.63362069 0.625 0.56718346 0.41472868]
----------------------------------------
Trial 1729
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66882184 0.59482759 0.45977011 0.55297158 0.4618863 ]
----------------------------------------
Trial 1730
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
n_estimators=52, random_state=100,
subsample=0.9))])
cv score: [0.57471264 0.69827586 0.46982759 0.64470284 0.46382429]
----------------------------------------
Trial 1731
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
n_estimators=172, random_state=100,
subsample=0.95))])
cv score: [0.5545977 0.70258621 0.60488506 0.63178295 0.36046512]
----------------------------------------
Trial 1732
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.8))])
cv score: [0.67816092 0.63218391 0.49568966 0.66795866 0.41602067]
----------------------------------------
Trial 1733
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='log2',
n_estimators=145, random_state=100,
subsample=0.8))])
cv score: [0.58333333 0.61350575 0.47270115 0.56976744 0.38630491]
----------------------------------------
Trial 1734
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=180, random_state=100))])
cv score: [0.64224138 0.65804598 0.5862069 0.68217054 0.38242894]
----------------------------------------
Trial 1735
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=126,
random_state=100))])
cv score: [0.5545977 0.65948276 0.59195402 0.73901809 0.42571059]
----------------------------------------
Trial 1736
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74066092 0.68678161 0.71982759 0.63113695 0.42248062]
----------------------------------------
Trial 1737
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69971264 0.64224138 0.65373563 0.66925065 0.41085271]
----------------------------------------
Trial 1738
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=128, random_state=100,
subsample=0.7))])
cv score: [0.70833333 0.70258621 0.62212644 0.63565891 0.44573643]
----------------------------------------
Trial 1739
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=165, random_state=100))])
cv score: [0.56824713 0.67025862 0.58405172 0.60723514 0.43346253]
----------------------------------------
Trial 1740
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
n_estimators=136, random_state=100,
subsample=0.75))])
cv score: [0.58045977 0.65804598 0.49568966 0.67700258 0.41085271]
----------------------------------------
Trial 1741
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=81, random_state=100))])
cv score: [0.68678161 0.68247126 0.59626437 0.63565891 0.41989664]
----------------------------------------
Trial 1742
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=137,
random_state=100))])
cv score: [0.625 0.65804598 0.60201149 0.6744186 0.48191214]
----------------------------------------
Trial 1743
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64511494 0.68534483 0.54454023 0.62661499 0.40826873]
----------------------------------------
Trial 1744
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
n_estimators=156, random_state=100,
subsample=0.9))])
cv score: [0.52729885 0.75718391 0.61063218 0.67312661 0.28682171]
----------------------------------------
Trial 1745
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=119, random_state=100,
subsample=0.6))])
cv score: [0.58189655 0.69109195 0.59626437 0.68475452 0.47416021]
----------------------------------------
Trial 1746
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=116, random_state=100))])
cv score: [0.55316092 0.67313218 0.60847701 0.58268734 0.39534884]
----------------------------------------
Trial 1747
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=131,
random_state=100))])
cv score: [0.58477011 0.69252874 0.58189655 0.66925065 0.42635659]
----------------------------------------
Trial 1748
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=107,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63793103 0.65373563 0.57614943 0.58268734 0.4496124 ]
----------------------------------------
Trial 1749
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.72557471 0.58908046 0.61886305 0.43669251]
----------------------------------------
Trial 1750
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=154, random_state=100))])
cv score: [0.59554598 0.73275862 0.63936782 0.6879845 0.42118863]
----------------------------------------
Trial 1751
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=166,
random_state=100))])
cv score: [0.61350575 0.6637931 0.6170977 0.7248062 0.40568475]
----------------------------------------
Trial 1752
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=134,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66235632 0.70258621 0.58045977 0.59302326 0.38113695]
----------------------------------------
Trial 1753
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=84,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72701149 0.65804598 0.70545977 0.60529716 0.41925065]
----------------------------------------
Trial 1754
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=172,
random_state=100))])
cv score: [0.68031609 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 1755
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=92,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69971264 0.60847701 0.6875 0.64857881 0.46770026]
----------------------------------------
Trial 1756
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=59, random_state=100))])
cv score: [0.57758621 0.62859195 0.55747126 0.63307494 0.43281654]
----------------------------------------
Trial 1757
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=196,
random_state=100))])
cv score: [0.63936782 0.67241379 0.61206897 0.63436693 0.43152455]
----------------------------------------
Trial 1758
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=162,
random_state=100))])
cv score: [0.63721264 0.59841954 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 1759
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=44, random_state=100))])
cv score: [0.5316092 0.61781609 0.64655172 0.72222222 0.33333333]
----------------------------------------
Trial 1760
----------------------------------------
Parameters {'rf__n_estimators': 125, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=125,
random_state=100))])
cv score: [0.61206897 0.68103448 0.61350575 0.71317829 0.4250646 ]
----------------------------------------
Trial 1761
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=90,
random_state=100))])
cv score: [0.55316092 0.67816092 0.63864943 0.72609819 0.3501292 ]
----------------------------------------
Trial 1762
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=141, random_state=100,
subsample=0.8))])
cv score: [0.55316092 0.67816092 0.42385057 0.58268734 0.49870801]
----------------------------------------
Trial 1763
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=94, random_state=100,
subsample=0.95))])
cv score: [0.54885057 0.67241379 0.51867816 0.59043928 0.40439276]
----------------------------------------
Trial 1764
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=44,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 1765
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=113,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66235632 0.56178161 0.66882184 0.52196382 0.37080103]
----------------------------------------
Trial 1766
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=54,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.74281609 0.43390805 0.62661499 0.47157623]
----------------------------------------
Trial 1767
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=30,
random_state=100))])
cv score: [0.71551724 0.61925287 0.57614943 0.61757106 0.37338501]
----------------------------------------
Trial 1768
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7183908 0.59195402 0.49928161 0.54651163 0.36627907]
----------------------------------------
Trial 1769
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=31, random_state=100))])
cv score: [0.64367816 0.63362069 0.42385057 0.65891473 0.44573643]
----------------------------------------
Trial 1770
----------------------------------------
Parameters {'rf__n_estimators': 138, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=138, random_state=100))])
cv score: [0.72413793 0.63936782 0.66954023 0.59043928 0.41731266]
----------------------------------------
Trial 1771
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=124, random_state=100))])
cv score: [0.62068966 0.69396552 0.66522989 0.67571059 0.42700258]
----------------------------------------
Trial 1772
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=83,
random_state=100))])
cv score: [0.62643678 0.67385057 0.61925287 0.64728682 0.39534884]
----------------------------------------
Trial 1773
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=194, random_state=100))])
cv score: [0.64511494 0.66810345 0.58333333 0.68604651 0.39018088]
----------------------------------------
Trial 1774
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=191,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67600575 0.5862069 0.68175287 0.53488372 0.37209302]
----------------------------------------
Trial 1775
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='log2',
n_estimators=63, random_state=100,
subsample=0.8))])
cv score: [0.70833333 0.55890805 0.65948276 0.66795866 0.46640827]
----------------------------------------
Trial 1776
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=86, random_state=100))])
cv score: [0.74281609 0.62787356 0.6637931 0.61627907 0.36950904]
----------------------------------------
Trial 1777
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=167, random_state=100,
subsample=0.6))])
cv score: [0.58477011 0.72701149 0.63505747 0.68217054 0.47932817]
----------------------------------------
Trial 1778
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=28, random_state=100))])
cv score: [0.61494253 0.67385057 0.50359195 0.70155039 0.45865633]
----------------------------------------
Trial 1779
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=192, random_state=100))])
cv score: [0.66091954 0.63505747 0.60201149 0.60852713 0.40568475]
----------------------------------------
Trial 1780
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=35,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60847701 0.69181034 0.70114943 0.55684755 0.38565891]
----------------------------------------
Trial 1781
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73922414 0.56896552 0.64583333 0.60594315 0.38242894]
----------------------------------------
Trial 1782
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=126, random_state=100,
subsample=0.65))])
cv score: [0.63362069 0.63218391 0.52298851 0.52842377 0.4250646 ]
----------------------------------------
Trial 1783
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=72, random_state=100))])
cv score: [0.63793103 0.62212644 0.61063218 0.64470284 0.38113695]
----------------------------------------
Trial 1784
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=87,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.66235632 0.68965517 0.6369509 0.38242894]
----------------------------------------
Trial 1785
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='sqrt',
n_estimators=143, random_state=100,
subsample=0.95))])
cv score: [0.66091954 0.62931034 0.63505747 0.62144703 0.40568475]
----------------------------------------
Trial 1786
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.6637931 0.5933908 0.63307494 0.4379845 ]
----------------------------------------
Trial 1787
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74568966 0.61637931 0.73060345 0.56072351 0.39922481]
----------------------------------------
Trial 1788
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.73850575 0.55603448 0.63436693 0.40956072]
----------------------------------------
Trial 1789
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56465517 0.74137931 0.63649425 0.625323 0.40826873]
----------------------------------------
Trial 1790
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=94,
random_state=100))])
cv score: [0.61925287 0.64798851 0.58764368 0.66020672 0.41602067]
----------------------------------------
Trial 1791
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=123, random_state=100,
subsample=0.8))])
cv score: [0.57902299 0.64511494 0.69683908 0.58010336 0.40310078]
----------------------------------------
Trial 1792
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=187, random_state=100))])
cv score: [0.71551724 0.64511494 0.65229885 0.58268734 0.42118863]
----------------------------------------
Trial 1793
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=180,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.65229885 0.5862069 0.65633075 0.40826873]
----------------------------------------
Trial 1794
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=148, random_state=100,
subsample=0.8))])
cv score: [0.69252874 0.67385057 0.67528736 0.64211886 0.41343669]
----------------------------------------
Trial 1795
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=145,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.65229885 0.67528736 0.65503876 0.41602067]
----------------------------------------
Trial 1796
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=101, random_state=100))])
cv score: [0.59195402 0.65804598 0.57902299 0.62144703 0.44573643]
----------------------------------------
Trial 1797
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=134,
random_state=100))])
cv score: [0.77155172 0.63362069 0.6716954 0.64082687 0.39147287]
----------------------------------------
Trial 1798
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='log2',
n_estimators=27, random_state=100,
subsample=0.75))])
cv score: [0.5 0.62931034 0.51293103 0.41989664 0.41085271]
----------------------------------------
Trial 1799
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=69,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.64511494 0.55747126 0.53617571 0.44573643]
----------------------------------------
Trial 1800
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=45,
random_state=100,
subsample=0.95))])
cv score: [0.53735632 0.67816092 0.53304598 0.70801034 0.46382429]
----------------------------------------
Trial 1801
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55316092 0.59913793 0.5545977 0.73385013 0.46770026]
----------------------------------------
Trial 1802
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=12,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.56896552 0.56752874 0.67829457 0.51937984]
----------------------------------------
Trial 1803
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=193,
random_state=100))])
cv score: [0.63505747 0.66810345 0.61925287 0.63565891 0.43152455]
----------------------------------------
Trial 1804
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=158, random_state=100,
subsample=0.6))])
cv score: [0.78017241 0.62068966 0.75574713 0.61369509 0.41085271]
----------------------------------------
Trial 1805
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=88,
random_state=100))])
cv score: [0.64942529 0.63218391 0.61925287 0.63565891 0.43927649]
----------------------------------------
Trial 1806
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=111, random_state=100))])
cv score: [0.56321839 0.67241379 0.6091954 0.59431525 0.40439276]
----------------------------------------
Trial 1807
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='sqrt',
n_estimators=16, random_state=100,
subsample=0.9))])
cv score: [0.64224138 0.625 0.68821839 0.60723514 0.41472868]
----------------------------------------
Trial 1808
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.7341954 0.60775862 0.60723514 0.42635659]
----------------------------------------
Trial 1809
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=148,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.72413793 0.55172414 0.60594315 0.41602067]
----------------------------------------
Trial 1810
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=150,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77514368 0.61566092 0.69109195 0.62403101 0.4496124 ]
----------------------------------------
Trial 1811
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=172,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74568966 0.63074713 0.69252874 0.63436693 0.46059432]
----------------------------------------
Trial 1812
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=105,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72413793 0.67241379 0.67241379 0.58397933 0.46640827]
----------------------------------------
Trial 1813
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=57,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77801724 0.54885057 0.66235632 0.64082687 0.39793282]
----------------------------------------
Trial 1814
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=36,
random_state=100))])
cv score: [0.57183908 0.6012931 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 1815
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=183, random_state=100,
subsample=0.9))])
cv score: [0.67816092 0.66091954 0.48994253 0.60723514 0.51033592]
----------------------------------------
Trial 1816
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=53, random_state=100,
subsample=0.95))])
cv score: [0.60057471 0.69252874 0.69827586 0.71834625 0.38630491]
----------------------------------------
Trial 1817
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=174,
random_state=100))])
cv score: [0.61997126 0.59913793 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 1818
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=77, random_state=100))])
cv score: [0.63218391 0.66954023 0.60775862 0.66795866 0.42377261]
----------------------------------------
Trial 1819
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='log2',
n_estimators=74, random_state=100,
subsample=0.85))])
cv score: [0.62212644 0.63074713 0.50574713 0.7118863 0.4496124 ]
----------------------------------------
Trial 1820
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=67, random_state=100))])
cv score: [0.53735632 0.6329023 0.60344828 0.55555556 0.42183463]
----------------------------------------
Trial 1821
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=31, random_state=100))])
cv score: [0.61925287 0.625 0.71264368 0.5626615 0.40439276]
----------------------------------------
Trial 1822
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=193, random_state=100))])
cv score: [0.59770115 0.73204023 0.63936782 0.67571059 0.4379845 ]
----------------------------------------
Trial 1823
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=87, random_state=100))])
cv score: [0.59626437 0.66235632 0.56178161 0.6124031 0.44444444]
----------------------------------------
Trial 1824
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=177, random_state=100))])
cv score: [0.59841954 0.73563218 0.64655172 0.6744186 0.44186047]
----------------------------------------
Trial 1825
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=21, random_state=100))])
cv score: [0.57471264 0.65517241 0.55100575 0.62403101 0.51937984]
----------------------------------------
Trial 1826
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=88,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72557471 0.65804598 0.64798851 0.64470284 0.43927649]
----------------------------------------
Trial 1827
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=155,
random_state=100))])
cv score: [0.65517241 0.63649425 0.63505747 0.63824289 0.4496124 ]
----------------------------------------
Trial 1828
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72413793 0.58333333 0.61350575 0.58010336 0.47997416]
----------------------------------------
Trial 1829
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=135, random_state=100,
subsample=0.95))])
cv score: [0.66954023 0.68821839 0.74137931 0.62661499 0.40697674]
----------------------------------------
Trial 1830
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='sqrt',
n_estimators=58, random_state=100,
subsample=0.65))])
cv score: [0.40086207 0.63218391 0.77155172 0.53875969 0.37984496]
----------------------------------------
Trial 1831
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=35, random_state=100,
subsample=0.6))])
cv score: [0.61063218 0.62931034 0.51005747 0.59173127 0.48191214]
----------------------------------------
Trial 1832
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=65,
random_state=100))])
cv score: [0.59626437 0.66091954 0.61135057 0.70801034 0.41149871]
----------------------------------------
Trial 1833
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=78, random_state=100))])
cv score: [0.73132184 0.61063218 0.67672414 0.55555556 0.39147287]
----------------------------------------
Trial 1834
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=72,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73275862 0.6637931 0.71982759 0.59496124 0.47222222]
----------------------------------------
Trial 1835
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='sqrt',
n_estimators=192, random_state=100,
subsample=0.75))])
cv score: [0.6408046 0.63793103 0.5 0.59431525 0.45736434]
----------------------------------------
Trial 1836
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
random_state=100))])
cv score: [0.75143678 0.63505747 0.6637931 0.57622739 0.4005168 ]
----------------------------------------
Trial 1837
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=162,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67672414 0.74425287 0.55747126 0.46382429 0.47028424]
----------------------------------------
Trial 1838
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75574713 0.6954023 0.68031609 0.57881137 0.375323 ]
----------------------------------------
Trial 1839
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=32, random_state=100))])
cv score: [0.59482759 0.69109195 0.57183908 0.6621447 0.45348837]
----------------------------------------
Trial 1840
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
n_estimators=199, random_state=100,
subsample=0.9))])
cv score: [0.54166667 0.74712644 0.60775862 0.63178295 0.31653747]
----------------------------------------
Trial 1841
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.62643678 0.62931034 0.61111111 0.41472868]
----------------------------------------
Trial 1842
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=170, random_state=100))])
cv score: [0.73132184 0.63649425 0.65804598 0.59043928 0.40697674]
----------------------------------------
Trial 1843
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=60,
random_state=100))])
cv score: [0.66091954 0.55028736 0.65229885 0.5245478 0.49483204]
----------------------------------------
Trial 1844
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=56,
random_state=100))])
cv score: [0.68318966 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 1845
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=181,
random_state=100))])
cv score: [0.54166667 0.65517241 0.5933908 0.74289406 0.40245478]
----------------------------------------
Trial 1846
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
n_estimators=183, random_state=100,
subsample=0.9))])
cv score: [0.62643678 0.68821839 0.59051724 0.63307494 0.41343669]
----------------------------------------
Trial 1847
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55316092 0.75287356 0.66235632 0.61175711 0.40826873]
----------------------------------------
Trial 1848
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=105, random_state=100))])
cv score: [0.68390805 0.6795977 0.60057471 0.63565891 0.41989664]
----------------------------------------
Trial 1849
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=140,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75071839 0.56896552 0.68247126 0.63372093 0.42764858]
----------------------------------------
Trial 1850
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=126,
random_state=100))])
cv score: [0.77729885 0.63362069 0.66882184 0.6369509 0.39793282]
----------------------------------------
Trial 1851
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=93, random_state=100))])
cv score: [0.60344828 0.69827586 0.64727011 0.66472868 0.41860465]
----------------------------------------
Trial 1852
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=184, random_state=100))])
cv score: [0.76867816 0.6408046 0.56034483 0.62661499 0.40762274]
----------------------------------------
Trial 1853
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='log2',
n_estimators=60, random_state=100,
subsample=0.65))])
cv score: [0.56321839 0.56465517 0.46551724 0.80103359 0.37726098]
----------------------------------------
Trial 1854
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75933908 0.60057471 0.60991379 0.56524548 0.38178295]
----------------------------------------
Trial 1855
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=116, random_state=100))])
cv score: [0.56178161 0.68678161 0.60991379 0.59237726 0.43023256]
----------------------------------------
Trial 1856
----------------------------------------
Parameters {'gb__n_estimators': 88, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=88, random_state=100,
subsample=0.9))])
cv score: [0.65229885 0.69971264 0.58189655 0.64599483 0.42377261]
----------------------------------------
Trial 1857
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=196,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.69252874 0.56465517 0.64211886 0.42118863]
----------------------------------------
Trial 1858
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=194, random_state=100,
subsample=0.7))])
cv score: [0.75718391 0.65086207 0.55316092 0.60852713 0.41860465]
----------------------------------------
Trial 1859
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=189, random_state=100,
subsample=0.65))])
cv score: [0.6637931 0.67816092 0.64655172 0.65633075 0.50129199]
----------------------------------------
Trial 1860
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=37,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76867816 0.57255747 0.68893678 0.60594315 0.36757106]
----------------------------------------
Trial 1861
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=50,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80387931 0.65229885 0.68103448 0.60400517 0.47222222]
----------------------------------------
Trial 1862
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=141,
random_state=100))])
cv score: [0.75143678 0.64942529 0.69396552 0.64341085 0.43927649]
----------------------------------------
Trial 1863
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='sqrt',
n_estimators=21, random_state=100,
subsample=0.7))])
cv score: [0.60057471 0.6091954 0.49137931 0.58010336 0.45607235]
----------------------------------------
Trial 1864
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=166, random_state=100))])
cv score: [0.56681034 0.67097701 0.60488506 0.60206718 0.43669251]
----------------------------------------
Trial 1865
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=110, random_state=100))])
cv score: [0.62787356 0.67672414 0.60344828 0.66408269 0.42377261]
----------------------------------------
Trial 1866
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=37,
random_state=100))])
cv score: [0.60057471 0.64942529 0.625 0.57235142 0.43927649]
----------------------------------------
Trial 1867
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=138, random_state=100,
subsample=0.65))])
cv score: [0.63362069 0.55316092 0.70258621 0.6124031 0.51679587]
----------------------------------------
Trial 1868
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=106, random_state=100,
subsample=0.9))])
cv score: [0.73132184 0.64798851 0.70689655 0.63824289 0.39922481]
----------------------------------------
Trial 1869
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=179,
random_state=100))])
cv score: [0.6170977 0.59985632 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 1870
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=76, random_state=100))])
cv score: [0.58261494 0.65948276 0.60775862 0.58268734 0.43992248]
----------------------------------------
Trial 1871
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=137, random_state=100,
subsample=0.8))])
cv score: [0.66091954 0.66235632 0.47701149 0.62790698 0.46640827]
----------------------------------------
Trial 1872
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=126, random_state=100))])
cv score: [0.60201149 0.72198276 0.63218391 0.67635659 0.4250646 ]
----------------------------------------
Trial 1873
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='sqrt',
n_estimators=54, random_state=100,
subsample=0.9))])
cv score: [0.74856322 0.60488506 0.52729885 0.59043928 0.47803618]
----------------------------------------
Trial 1874
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=39,
random_state=100))])
cv score: [0.79382184 0.62571839 0.70402299 0.66343669 0.41085271]
----------------------------------------
Trial 1875
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='log2',
n_estimators=23, random_state=100,
subsample=0.65))])
cv score: [0.56465517 0.60344828 0.60488506 0.58914729 0.4754522 ]
----------------------------------------
Trial 1876
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=156, random_state=100))])
cv score: [0.5933908 0.70043103 0.66666667 0.70542636 0.41537468]
----------------------------------------
Trial 1877
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=46, random_state=100))])
cv score: [0.59626437 0.61494253 0.63793103 0.71059432 0.33074935]
----------------------------------------
Trial 1878
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=66, random_state=100))])
cv score: [0.62931034 0.60488506 0.58045977 0.59043928 0.43540052]
----------------------------------------
Trial 1879
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=198,
random_state=100))])
cv score: [0.77155172 0.64224138 0.69252874 0.63953488 0.39276486]
----------------------------------------
Trial 1880
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=164, random_state=100))])
cv score: [0.73563218 0.63649425 0.66522989 0.58397933 0.40439276]
----------------------------------------
Trial 1881
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=72, random_state=100))])
cv score: [0.74712644 0.66091954 0.67241379 0.6369509 0.4496124 ]
----------------------------------------
Trial 1882
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=130, random_state=100,
subsample=0.85))])
cv score: [0.57471264 0.69683908 0.61637931 0.71317829 0.41860465]
----------------------------------------
Trial 1883
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=49, random_state=100,
subsample=0.85))])
cv score: [0.65086207 0.68534483 0.77801724 0.59819121 0.47286822]
----------------------------------------
Trial 1884
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
n_estimators=110, random_state=100,
subsample=0.65))])
cv score: [0.56178161 0.63649425 0.59770115 0.62144703 0.56976744]
----------------------------------------
Trial 1885
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=145, random_state=100))])
cv score: [0.5625 0.68103448 0.61350575 0.5994832 0.4502584 ]
----------------------------------------
Trial 1886
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=124,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79454023 0.6012931 0.67816092 0.62596899 0.46576227]
----------------------------------------
Trial 1887
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=80, random_state=100))])
cv score: [0.62931034 0.68390805 0.60488506 0.66537468 0.43152455]
----------------------------------------
Trial 1888
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=73, random_state=100))])
cv score: [0.60201149 0.64798851 0.65229885 0.59431525 0.41472868]
----------------------------------------
Trial 1889
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=140, random_state=100,
subsample=0.9))])
cv score: [0.61781609 0.67385057 0.40229885 0.65116279 0.4379845 ]
----------------------------------------
Trial 1890
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.72126437 0.63793103 0.67385057 0.58527132 0.41472868]
----------------------------------------
Trial 1891
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='sqrt',
n_estimators=22, random_state=100,
subsample=0.85))])
cv score: [0.50143678 0.51724138 0.4612069 0.70284238 0.49483204]
----------------------------------------
Trial 1892
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=89,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52155172 0.72988506 0.73994253 0.62273902 0.44573643]
----------------------------------------
Trial 1893
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=163, random_state=100))])
cv score: [0.68247126 0.66810345 0.64224138 0.68992248 0.4496124 ]
----------------------------------------
Trial 1894
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=138, random_state=100,
subsample=0.6))])
cv score: [0.68247126 0.625 0.44252874 0.67571059 0.55813953]
----------------------------------------
Trial 1895
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=127, random_state=100,
subsample=0.9))])
cv score: [0.58045977 0.69971264 0.49425287 0.68992248 0.45607235]
----------------------------------------
Trial 1896
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=47,
random_state=100))])
cv score: [0.61278736 0.60632184 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 1897
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=30, random_state=100))])
cv score: [0.55387931 0.65014368 0.5158046 0.58010336 0.47093023]
----------------------------------------
Trial 1898
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.72270115 0.72701149 0.62661499 0.37855297]
----------------------------------------
Trial 1899
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=122, random_state=100,
subsample=0.65))])
cv score: [0.77011494 0.64367816 0.64655172 0.60981912 0.3875969 ]
----------------------------------------
Trial 1900
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=134, random_state=100,
subsample=0.65))])
cv score: [0.51436782 0.6566092 0.67816092 0.76873385 0.3501292 ]
----------------------------------------
Trial 1901
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=96,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5933908 0.71264368 0.59051724 0.61627907 0.43540052]
----------------------------------------
Trial 1902
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=168, random_state=100,
subsample=0.75))])
cv score: [0.75862069 0.6408046 0.73706897 0.62209302 0.39341085]
----------------------------------------
Trial 1903
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=41, random_state=100,
subsample=0.6))])
cv score: [0.57902299 0.68103448 0.58333333 0.71059432 0.46770026]
----------------------------------------
Trial 1904
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=117,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68678161 0.62356322 0.64942529 0.53617571 0.45607235]
----------------------------------------
Trial 1905
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=60,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.68534483 0.61637931 0.6873385 0.40568475]
----------------------------------------
Trial 1906
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.69683908 0.61637931 0.59173127 0.4379845 ]
----------------------------------------
Trial 1907
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=171,
random_state=100))])
cv score: [0.58045977 0.69252874 0.50143678 0.60723514 0.43281654]
----------------------------------------
Trial 1908
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=191,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56321839 0.65086207 0.55316092 0.55426357 0.4121447 ]
----------------------------------------
Trial 1909
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=50,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.67385057 0.53017241 0.56718346 0.39664083]
----------------------------------------
Trial 1910
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=81,
random_state=100))])
cv score: [0.62068966 0.63936782 0.59051724 0.64857881 0.39147287]
----------------------------------------
Trial 1911
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=197,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73994253 0.69396552 0.71982759 0.60271318 0.37855297]
----------------------------------------
Trial 1912
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=42,
random_state=100))])
cv score: [0.68390805 0.62931034 0.68678161 0.68475452 0.37726098]
----------------------------------------
Trial 1913
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=121,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.66235632 0.64942529 0.60077519 0.37984496]
----------------------------------------
Trial 1914
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=107, random_state=100,
subsample=0.6))])
cv score: [0.52442529 0.63505747 0.34051724 0.54328165 0.49483204]
----------------------------------------
Trial 1915
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=136,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.7112069 0.61781609 0.59689922 0.39793282]
----------------------------------------
Trial 1916
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
n_estimators=97, random_state=100,
subsample=0.7))])
cv score: [0.55603448 0.68821839 0.53304598 0.66666667 0.4250646 ]
----------------------------------------
Trial 1917
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=22, random_state=100,
subsample=0.95))])
cv score: [0.60201149 0.67241379 0.5862069 0.69896641 0.49483204]
----------------------------------------
Trial 1918
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=172,
random_state=100))])
cv score: [0.61637931 0.65948276 0.61278736 0.72351421 0.41860465]
----------------------------------------
Trial 1919
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=97,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72126437 0.6566092 0.70258621 0.64470284 0.39147287]
----------------------------------------
Trial 1920
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=166, random_state=100,
subsample=0.95))])
cv score: [0.56178161 0.73275862 0.5933908 0.65633075 0.47803618]
----------------------------------------
Trial 1921
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=84,
random_state=100))])
cv score: [0.61494253 0.68821839 0.61063218 0.7118863 0.36821705]
----------------------------------------
Trial 1922
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=103,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.73132184 0.62931034 0.62661499 0.43023256]
----------------------------------------
Trial 1923
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=86, random_state=100,
subsample=0.65))])
cv score: [0.59482759 0.57471264 0.54022989 0.73385013 0.46640827]
----------------------------------------
Trial 1924
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=131,
random_state=100))])
cv score: [0.73132184 0.64798851 0.58045977 0.62919897 0.41731266]
----------------------------------------
Trial 1925
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=67, random_state=100,
subsample=0.75))])
cv score: [0.64798851 0.70977011 0.53017241 0.68346253 0.44056848]
----------------------------------------
Trial 1926
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=137,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.7112069 0.59626437 0.64470284 0.39276486]
----------------------------------------
Trial 1927
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=187, random_state=100,
subsample=0.75))])
cv score: [0.64655172 0.5933908 0.6795977 0.6744186 0.35142119]
----------------------------------------
Trial 1928
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=94,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 1929
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=197,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60201149 0.69827586 0.61063218 0.53229974 0.42377261]
----------------------------------------
Trial 1930
----------------------------------------
Parameters {'xgb__n_estimators': 171, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=171,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.66810345 0.59913793 0.54005168 0.37855297]
----------------------------------------
Trial 1931
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=95, random_state=100,
subsample=0.8))])
cv score: [0.60488506 0.69109195 0.59482759 0.73126615 0.44444444]
----------------------------------------
Trial 1932
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=36,
random_state=100))])
cv score: [0.56321839 0.6091954 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 1933
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=39, random_state=100))])
cv score: [0.58333333 0.64942529 0.59770115 0.56718346 0.50775194]
----------------------------------------
Trial 1934
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=163,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.70977011 0.59051724 0.59431525 0.45348837]
----------------------------------------
Trial 1935
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=103,
random_state=100))])
cv score: [0.51724138 0.67241379 0.61781609 0.72739018 0.47286822]
----------------------------------------
Trial 1936
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=46, random_state=100,
subsample=0.7))])
cv score: [0.625 0.68965517 0.50862069 0.60852713 0.4870801 ]
----------------------------------------
Trial 1937
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=99, random_state=100))])
cv score: [0.59985632 0.69612069 0.64511494 0.66537468 0.42894057]
----------------------------------------
Trial 1938
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=55,
random_state=100))])
cv score: [0.58045977 0.63362069 0.6795977 0.68927649 0.37726098]
----------------------------------------
Trial 1939
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=119,
random_state=100))])
cv score: [0.70545977 0.64798851 0.65948276 0.65503876 0.42118863]
----------------------------------------
Trial 1940
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=54,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.65086207 0.75287356 0.63436693 0.39018088]
----------------------------------------
Trial 1941
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=47,
random_state=100))])
cv score: [0.59913793 0.64295977 0.63649425 0.67894057 0.41989664]
----------------------------------------
Trial 1942
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=185,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 1943
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=76, random_state=100))])
cv score: [0.6566092 0.59913793 0.5704023 0.58914729 0.43281654]
----------------------------------------
Trial 1944
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=110,
random_state=100))])
cv score: [0.60344828 0.65948276 0.61350575 0.65633075 0.42635659]
----------------------------------------
Trial 1945
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=86, random_state=100,
subsample=0.95))])
cv score: [0.62068966 0.69252874 0.61063218 0.625323 0.4625323 ]
----------------------------------------
Trial 1946
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=154,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77586207 0.63074713 0.75502874 0.64147287 0.37015504]
----------------------------------------
Trial 1947
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='log2',
n_estimators=53, random_state=100,
subsample=0.9))])
cv score: [0.52442529 0.66522989 0.51867816 0.51162791 0.38501292]
----------------------------------------
Trial 1948
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
n_estimators=109, random_state=100,
subsample=0.8))])
cv score: [0.57183908 0.72988506 0.63793103 0.70155039 0.47674419]
----------------------------------------
Trial 1949
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=129, random_state=100))])
cv score: [0.75 0.63505747 0.66235632 0.64082687 0.41472868]
----------------------------------------
Trial 1950
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78951149 0.61997126 0.68103448 0.61757106 0.43217054]
----------------------------------------
Trial 1951
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=50, random_state=100))])
cv score: [0.73563218 0.62715517 0.63649425 0.60465116 0.39341085]
----------------------------------------
Trial 1952
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=168, random_state=100,
subsample=0.6))])
cv score: [0.40948276 0.64655172 0.44252874 0.62273902 0.52713178]
----------------------------------------
Trial 1953
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=128, random_state=100,
subsample=0.85))])
cv score: [0.52729885 0.67528736 0.56321839 0.60723514 0.39405685]
----------------------------------------
Trial 1954
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=199,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.6795977 0.59195402 0.62790698 0.43023256]
----------------------------------------
Trial 1955
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='log2',
n_estimators=153, random_state=100,
subsample=0.75))])
cv score: [0.69109195 0.67672414 0.57902299 0.66020672 0.44056848]
----------------------------------------
Trial 1956
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=92,
random_state=100,
subsample=0.65))])
cv score: [0.66666667 0.49568966 0.64942529 0.64857881 0.46511628]
----------------------------------------
Trial 1957
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.6954023 0.65373563 0.63436693 0.44186047]
----------------------------------------
Trial 1958
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=165, random_state=100))])
cv score: [0.74137931 0.63649425 0.6637931 0.58139535 0.40439276]
----------------------------------------
Trial 1959
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=105, random_state=100))])
cv score: [0.6091954 0.6875 0.65086207 0.6873385 0.43152455]
----------------------------------------
Trial 1960
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=147,
random_state=100))])
cv score: [0.69396552 0.67385057 0.77801724 0.64857881 0.37855297]
----------------------------------------
Trial 1961
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=23,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.6487069 0.56752874 0.59043928 0.4877261 ]
----------------------------------------
Trial 1962
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=50,
random_state=100))])
cv score: [0.55747126 0.62643678 0.50143678 0.64728682 0.38888889]
----------------------------------------
Trial 1963
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=122,
random_state=100))])
cv score: [0.61781609 0.65948276 0.58908046 0.66149871 0.48578811]
----------------------------------------
Trial 1964
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=30,
random_state=100))])
cv score: [0.56321839 0.6091954 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 1965
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='log2',
n_estimators=101, random_state=100,
subsample=0.7))])
cv score: [0.75574713 0.66091954 0.59913793 0.64599483 0.49224806]
----------------------------------------
Trial 1966
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=101,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.67097701 0.53735632 0.49224806 0.35658915]
----------------------------------------
Trial 1967
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=136,
random_state=100))])
cv score: [0.54454023 0.65948276 0.58333333 0.7377261 0.42054264]
----------------------------------------
Trial 1968
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=183, random_state=100,
subsample=0.7))])
cv score: [0.74856322 0.64511494 0.65373563 0.63824289 0.43152455]
----------------------------------------
Trial 1969
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67528736 0.61781609 0.53591954 0.63565891 0.38242894]
----------------------------------------
Trial 1970
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=164,
random_state=100))])
cv score: [0.65086207 0.63793103 0.61925287 0.65116279 0.45219638]
----------------------------------------
Trial 1971
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=149, random_state=100))])
cv score: [0.57758621 0.65373563 0.5862069 0.6369509 0.44702842]
----------------------------------------
Trial 1972
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56609195 0.6795977 0.46982759 0.54134367 0.36692506]
----------------------------------------
Trial 1973
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=142, random_state=100,
subsample=0.75))])
cv score: [0.66091954 0.67528736 0.62068966 0.62790698 0.42635659]
----------------------------------------
Trial 1974
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=186, random_state=100,
subsample=0.8))])
cv score: [0.67816092 0.72988506 0.68534483 0.60723514 0.47286822]
----------------------------------------
Trial 1975
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=121,
random_state=100))])
cv score: [0.59770115 0.67385057 0.63362069 0.65633075 0.43281654]
----------------------------------------
Trial 1976
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=117, random_state=100))])
cv score: [0.74856322 0.63793103 0.66666667 0.58010336 0.39018088]
----------------------------------------
Trial 1977
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=14,
random_state=100))])
cv score: [0.64295977 0.59841954 0.55747126 0.51744186 0.41860465]
----------------------------------------
Trial 1978
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=170, random_state=100,
subsample=0.75))])
cv score: [0.44755747 0.61925287 0.50574713 0.61498708 0.57881137]
----------------------------------------
Trial 1979
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.60632184 0.71408046 0.62919897 0.45607235]
----------------------------------------
Trial 1980
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=152, random_state=100))])
cv score: [0.72126437 0.63936782 0.6637931 0.56718346 0.41343669]
----------------------------------------
Trial 1981
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74353448 0.5941092 0.70977011 0.55620155 0.41085271]
----------------------------------------
Trial 1982
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.72126437 0.61206897 0.62403101 0.40310078]
----------------------------------------
Trial 1983
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.7183908 0.57471264 0.62661499 0.40180879]
----------------------------------------
Trial 1984
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.67528736 0.52155172 0.54651163 0.37209302]
----------------------------------------
Trial 1985
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=51,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51293103 0.68390805 0.54885057 0.5994832 0.43281654]
----------------------------------------
Trial 1986
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.85))])
cv score: [0.49425287 0.68965517 0.38505747 0.63436693 0.48578811]
----------------------------------------
Trial 1987
----------------------------------------
Parameters {'gb__n_estimators': 177, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='sqrt',
n_estimators=177, random_state=100,
subsample=0.85))])
cv score: [0.62787356 0.65804598 0.53591954 0.67829457 0.45348837]
----------------------------------------
Trial 1988
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.67241379 0.63218391 0.67183463 0.43281654]
----------------------------------------
Trial 1989
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72485632 0.63433908 0.55028736 0.54328165 0.42312661]
----------------------------------------
Trial 1990
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=176,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.69109195 0.6408046 0.62919897 0.4379845 ]
----------------------------------------
Trial 1991
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=97,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80028736 0.55316092 0.70043103 0.65245478 0.40633075]
----------------------------------------
Trial 1992
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=58, random_state=100))])
cv score: [0.53232759 0.63649425 0.56752874 0.61692506 0.46576227]
----------------------------------------
Trial 1993
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=51,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.62931034 0.50143678 0.60981912 0.47157623]
----------------------------------------
Trial 1994
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.9))])
cv score: [0.64367816 0.77298851 0.58189655 0.56847545 0.43669251]
----------------------------------------
Trial 1995
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=58, random_state=100,
subsample=0.65))])
cv score: [0.64367816 0.67241379 0.64655172 0.6124031 0.47286822]
----------------------------------------
Trial 1996
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.48563218 0.47126437 0.53100775 0.41860465]
----------------------------------------
Trial 1997
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=86, random_state=100,
subsample=0.7))])
cv score: [0.60201149 0.68678161 0.54885057 0.58268734 0.44315245]
----------------------------------------
Trial 1998
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=35, random_state=100,
subsample=0.7))])
cv score: [0.6091954 0.63793103 0.57614943 0.65633075 0.38888889]
----------------------------------------
Trial 1999
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=100,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51867816 0.55028736 0.67385057 0.54651163 0.40826873]
----------------------------------------
Trial 2000
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=166, random_state=100,
subsample=0.6))])
cv score: [0.40948276 0.65086207 0.4454023 0.62403101 0.5374677 ]
----------------------------------------
Trial 2001
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72988506 0.66810345 0.6954023 0.60594315 0.44056848]
----------------------------------------
Trial 2002
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=47, random_state=100))])
cv score: [0.52442529 0.6408046 0.57614943 0.55490956 0.43540052]
----------------------------------------
Trial 2003
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=25, random_state=100,
subsample=0.75))])
cv score: [0.51724138 0.70402299 0.59195402 0.57105943 0.43927649]
----------------------------------------
Trial 2004
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=112, random_state=100,
subsample=0.95))])
cv score: [0.59770115 0.72413793 0.50287356 0.54263566 0.56072351]
----------------------------------------
Trial 2005
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=63, random_state=100,
subsample=0.9))])
cv score: [0.52729885 0.76005747 0.59482759 0.68863049 0.34302326]
----------------------------------------
Trial 2006
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=153,
random_state=100))])
cv score: [0.64942529 0.66235632 0.59195402 0.63953488 0.43669251]
----------------------------------------
Trial 2007
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=176, random_state=100))])
cv score: [0.60632184 0.72772989 0.65804598 0.67829457 0.43023256]
----------------------------------------
Trial 2008
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=57,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74568966 0.57686782 0.6716954 0.56072351 0.374677 ]
----------------------------------------
Trial 2009
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
n_estimators=98, random_state=100,
subsample=0.65))])
cv score: [0.55747126 0.71408046 0.58189655 0.66020672 0.46640827]
----------------------------------------
Trial 2010
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=114,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 2011
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=114,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.61925287 0.59051724 0.6124031 0.40956072]
----------------------------------------
Trial 2012
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=15,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56465517 0.60488506 0.58189655 0.52196382 0.51937984]
----------------------------------------
Trial 2013
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=23, random_state=100,
subsample=0.65))])
cv score: [0.60488506 0.72413793 0.67672414 0.57364341 0.31524548]
----------------------------------------
Trial 2014
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=184, random_state=100,
subsample=0.75))])
cv score: [0.67097701 0.73132184 0.5862069 0.63178295 0.4870801 ]
----------------------------------------
Trial 2015
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=159,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76364943 0.63362069 0.68103448 0.61498708 0.46447028]
----------------------------------------
Trial 2016
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=14, random_state=100,
subsample=0.75))])
cv score: [0.61063218 0.63218391 0.42097701 0.48062016 0.46511628]
----------------------------------------
Trial 2017
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
n_estimators=126, random_state=100,
subsample=0.65))])
cv score: [0.57758621 0.64655172 0.55028736 0.71447028 0.45994832]
----------------------------------------
Trial 2018
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=122,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7191092 0.59626437 0.72270115 0.58850129 0.39341085]
----------------------------------------
Trial 2019
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
random_state=100, subsample=0.7))])
cv score: [0.61781609 0.68103448 0.48563218 0.63565891 0.49224806]
----------------------------------------
Trial 2020
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=65,
random_state=100))])
cv score: [0.55603448 0.72701149 0.58333333 0.70284238 0.47157623]
----------------------------------------
Trial 2021
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=193,
random_state=100))])
cv score: [0.54741379 0.65517241 0.58189655 0.74031008 0.40439276]
----------------------------------------
Trial 2022
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=140, random_state=100,
subsample=0.95))])
cv score: [0.57471264 0.68103448 0.57471264 0.66795866 0.43927649]
----------------------------------------
Trial 2023
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=83, random_state=100))])
cv score: [0.62212644 0.68247126 0.61350575 0.67571059 0.42248062]
----------------------------------------
Trial 2024
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='sqrt',
n_estimators=12, random_state=100,
subsample=0.7))])
cv score: [0.625 0.72988506 0.63649425 0.58268734 0.51033592]
----------------------------------------
Trial 2025
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68534483 0.71551724 0.62356322 0.55943152 0.41343669]
----------------------------------------
Trial 2026
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=155, random_state=100,
subsample=0.8))])
cv score: [0.76867816 0.62356322 0.72988506 0.64599483 0.36692506]
----------------------------------------
Trial 2027
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=136,
random_state=100))])
cv score: [0.5933908 0.67385057 0.64655172 0.66149871 0.43152455]
----------------------------------------
Trial 2028
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=192, random_state=100,
subsample=0.9))])
cv score: [0.59626437 0.68103448 0.49856322 0.65633075 0.40956072]
----------------------------------------
Trial 2029
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=194, random_state=100,
subsample=0.75))])
cv score: [0.61781609 0.67097701 0.56896552 0.64857881 0.45994832]
----------------------------------------
Trial 2030
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=176,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.67385057 0.66666667 0.64470284 0.4005168 ]
----------------------------------------
Trial 2031
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.72557471 0.60632184 0.62403101 0.39664083]
----------------------------------------
Trial 2032
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='sqrt',
n_estimators=145, random_state=100,
subsample=0.95))])
cv score: [0.64655172 0.66235632 0.61781609 0.66795866 0.46511628]
----------------------------------------
Trial 2033
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.63362069 0.67241379 0.59237726 0.36563307]
----------------------------------------
Trial 2034
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=199,
random_state=100))])
cv score: [0.74568966 0.65229885 0.69683908 0.64599483 0.4379845 ]
----------------------------------------
Trial 2035
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=37,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.71982759 0.59626437 0.63049096 0.50258398]
----------------------------------------
Trial 2036
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=68,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.62931034 0.67385057 0.58527132 0.4005168 ]
----------------------------------------
Trial 2037
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=149, random_state=100))])
cv score: [0.72413793 0.64224138 0.67097701 0.59560724 0.40180879]
----------------------------------------
Trial 2038
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='sqrt',
n_estimators=190, random_state=100,
subsample=0.75))])
cv score: [0.69971264 0.64942529 0.58333333 0.63565891 0.45865633]
----------------------------------------
Trial 2039
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=70, random_state=100))])
cv score: [0.61350575 0.61494253 0.63218391 0.70542636 0.36692506]
----------------------------------------
Trial 2040
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55172414 0.56106322 0.56537356 0.55103359 0.47609819]
----------------------------------------
Trial 2041
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=89,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.7112069 0.51149425 0.52713178 0.45348837]
----------------------------------------
Trial 2042
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='log2',
n_estimators=79, random_state=100,
subsample=0.75))])
cv score: [0.60344828 0.65517241 0.60201149 0.62790698 0.41085271]
----------------------------------------
Trial 2043
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=62, random_state=100,
subsample=0.85))])
cv score: [0.65373563 0.63793103 0.61781609 0.6124031 0.50387597]
----------------------------------------
Trial 2044
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=16, random_state=100,
subsample=0.85))])
cv score: [0.68031609 0.64008621 0.55172414 0.63565891 0.35529716]
----------------------------------------
Trial 2045
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=77, random_state=100))])
cv score: [0.61781609 0.63649425 0.66091954 0.58268734 0.4121447 ]
----------------------------------------
Trial 2046
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=176,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 2047
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=144, random_state=100,
subsample=0.8))])
cv score: [0.58333333 0.74568966 0.5545977 0.69767442 0.41343669]
----------------------------------------
Trial 2048
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77083333 0.54741379 0.72772989 0.64470284 0.40374677]
----------------------------------------
Trial 2049
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=16, random_state=100,
subsample=0.6))])
cv score: [0.58045977 0.58764368 0.53304598 0.54909561 0.48449612]
----------------------------------------
Trial 2050
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=170, random_state=100))])
cv score: [0.63936782 0.6637931 0.6091954 0.61498708 0.45607235]
----------------------------------------
Trial 2051
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=14, random_state=100,
subsample=0.8))])
cv score: [0.51867816 0.46408046 0.48132184 0.63436693 0.48966408]
----------------------------------------
Trial 2052
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=141,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.68247126 0.59195402 0.62919897 0.39793282]
----------------------------------------
Trial 2053
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=43, random_state=100,
subsample=0.75))])
cv score: [0.59913793 0.63218391 0.60632184 0.5749354 0.44056848]
----------------------------------------
Trial 2054
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=162, random_state=100))])
cv score: [0.72701149 0.63936782 0.66091954 0.64211886 0.41472868]
----------------------------------------
Trial 2055
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=15, random_state=100))])
cv score: [0.66020115 0.63362069 0.57758621 0.60658915 0.38501292]
----------------------------------------
Trial 2056
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='sqrt',
n_estimators=196, random_state=100,
subsample=0.9))])
cv score: [0.57758621 0.69971264 0.42097701 0.60465116 0.45607235]
----------------------------------------
Trial 2057
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=166, random_state=100,
subsample=0.7))])
cv score: [0.6091954 0.69252874 0.55172414 0.68604651 0.42377261]
----------------------------------------
Trial 2058
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=52, random_state=100))])
cv score: [0.55531609 0.67025862 0.61206897 0.56330749 0.44832041]
----------------------------------------
Trial 2059
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=154, random_state=100,
subsample=0.9))])
cv score: [0.62068966 0.70402299 0.55028736 0.67054264 0.47803618]
----------------------------------------
Trial 2060
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=146, random_state=100))])
cv score: [0.61350575 0.72916667 0.66235632 0.68863049 0.40826873]
----------------------------------------
Trial 2061
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=122,
random_state=100))])
cv score: [0.61637931 0.59913793 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 2062
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='log2',
n_estimators=79, random_state=100,
subsample=0.75))])
cv score: [0.63793103 0.68821839 0.59195402 0.70542636 0.43152455]
----------------------------------------
Trial 2063
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.70258621 0.63074713 0.62015504 0.42635659]
----------------------------------------
Trial 2064
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=90, random_state=100,
subsample=0.85))])
cv score: [0.56321839 0.65804598 0.54310345 0.66666667 0.4379845 ]
----------------------------------------
Trial 2065
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='sqrt',
n_estimators=134, random_state=100,
subsample=0.7))])
cv score: [0.65373563 0.68534483 0.57902299 0.69767442 0.40826873]
----------------------------------------
Trial 2066
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=60, random_state=100,
subsample=0.85))])
cv score: [0.5862069 0.66091954 0.48275862 0.63565891 0.47286822]
----------------------------------------
Trial 2067
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=98,
random_state=100))])
cv score: [0.79597701 0.61925287 0.67025862 0.62403101 0.39276486]
----------------------------------------
Trial 2068
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7887931 0.55603448 0.60560345 0.52971576 0.39211886]
----------------------------------------
Trial 2069
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=93, random_state=100,
subsample=0.65))])
cv score: [0.67385057 0.72126437 0.5933908 0.62403101 0.47674419]
----------------------------------------
Trial 2070
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
n_estimators=52, random_state=100,
subsample=0.9))])
cv score: [0.68031609 0.72054598 0.67528736 0.62596899 0.40116279]
----------------------------------------
Trial 2071
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=179,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77729885 0.68678161 0.67528736 0.61821705 0.41731266]
----------------------------------------
Trial 2072
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=59,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51149425 0.70689655 0.50143678 0.55555556 0.40180879]
----------------------------------------
Trial 2073
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=137, random_state=100,
subsample=0.95))])
cv score: [0.60057471 0.71408046 0.57614943 0.72351421 0.40826873]
----------------------------------------
Trial 2074
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79597701 0.5933908 0.63074713 0.63113695 0.45865633]
----------------------------------------
Trial 2075
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=84, random_state=100,
subsample=0.95))])
cv score: [0.58045977 0.70114943 0.5 0.66408269 0.4250646 ]
----------------------------------------
Trial 2076
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.70689655 0.6091954 0.6124031 0.4250646 ]
----------------------------------------
Trial 2077
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='log2',
n_estimators=13, random_state=100,
subsample=0.65))])
cv score: [0.76149425 0.54454023 0.39511494 0.60852713 0.47803618]
----------------------------------------
Trial 2078
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=30, random_state=100,
subsample=0.8))])
cv score: [0.62931034 0.61925287 0.58477011 0.57364341 0.42894057]
----------------------------------------
Trial 2079
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=47,
random_state=100))])
cv score: [0.61637931 0.60704023 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 2080
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=174, random_state=100))])
cv score: [0.62931034 0.65229885 0.62068966 0.6744186 0.39534884]
----------------------------------------
Trial 2081
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
n_estimators=34,
random_state=100))])
cv score: [0.57327586 0.55531609 0.58333333 0.67054264 0.42118863]
----------------------------------------
Trial 2082
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=105, random_state=100,
subsample=0.9))])
cv score: [0.52011494 0.70545977 0.52586207 0.66020672 0.37855297]
----------------------------------------
Trial 2083
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=196,
random_state=100))])
cv score: [0.61278736 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 2084
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='sqrt',
n_estimators=11, random_state=100,
subsample=0.6))])
cv score: [0.77155172 0.5862069 0.61278736 0.66149871 0.40762274]
----------------------------------------
Trial 2085
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=144,
random_state=100))])
cv score: [0.59051724 0.67097701 0.64224138 0.68217054 0.42894057]
----------------------------------------
Trial 2086
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=107, random_state=100))])
cv score: [0.61637931 0.70977011 0.65373563 0.6744186 0.42118863]
----------------------------------------
Trial 2087
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=175, random_state=100,
subsample=0.7))])
cv score: [0.59482759 0.66235632 0.58333333 0.68087855 0.4754522 ]
----------------------------------------
Trial 2088
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=121, random_state=100))])
cv score: [0.76867816 0.63218391 0.61925287 0.63178295 0.4121447 ]
----------------------------------------
Trial 2089
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=190, random_state=100))])
cv score: [0.64655172 0.66810345 0.60201149 0.62273902 0.4625323 ]
----------------------------------------
Trial 2090
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=99,
random_state=100))])
cv score: [0.55316092 0.67456897 0.63074713 0.73255814 0.36563307]
----------------------------------------
Trial 2091
----------------------------------------
Parameters {'rf__n_estimators': 60, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=60,
random_state=100))])
cv score: [0.64798851 0.63218391 0.63793103 0.62403101 0.40439276]
----------------------------------------
Trial 2092
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.59913793 0.63505747 0.61757106 0.38372093]
----------------------------------------
Trial 2093
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=101, random_state=100,
subsample=0.9))])
cv score: [0.57327586 0.72701149 0.57471264 0.64857881 0.28036176]
----------------------------------------
Trial 2094
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=64, random_state=100))])
cv score: [0.57255747 0.70617816 0.63505747 0.68475452 0.43217054]
----------------------------------------
Trial 2095
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=92, random_state=100,
subsample=0.9))])
cv score: [0.68965517 0.68821839 0.47844828 0.63049096 0.43281654]
----------------------------------------
Trial 2096
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=188, random_state=100,
subsample=0.9))])
cv score: [0.60057471 0.73706897 0.61781609 0.60465116 0.34237726]
----------------------------------------
Trial 2097
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=187,
random_state=100,
subsample=0.85))])
cv score: [0.62643678 0.72557471 0.43390805 0.69896641 0.44056848]
----------------------------------------
Trial 2098
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.66810345 0.64224138 0.53875969 0.44186047]
----------------------------------------
Trial 2099
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=107, random_state=100))])
cv score: [0.75287356 0.64798851 0.65373563 0.62273902 0.39793282]
----------------------------------------
Trial 2100
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=108,
random_state=100))])
cv score: [0.78017241 0.63218391 0.67097701 0.63307494 0.39793282]
----------------------------------------
Trial 2101
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=87, random_state=100))])
cv score: [0.56752874 0.68821839 0.60775862 0.58914729 0.43346253]
----------------------------------------
Trial 2102
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='log2',
n_estimators=107,
random_state=100))])
cv score: [0.60344828 0.68390805 0.61063218 0.66925065 0.43281654]
----------------------------------------
Trial 2103
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=148,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.59913793 0.59051724 0.59431525 0.42118863]
----------------------------------------
Trial 2104
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.72413793 0.66020115 0.59366925 0.39211886]
----------------------------------------
Trial 2105
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64942529 0.74425287 0.63218391 0.48191214 0.43152455]
----------------------------------------
Trial 2106
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=155,
random_state=100))])
cv score: [0.77729885 0.63793103 0.68247126 0.6369509 0.39793282]
----------------------------------------
Trial 2107
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=192, random_state=100))])
cv score: [0.58548851 0.68821839 0.60488506 0.62855297 0.44573643]
----------------------------------------
Trial 2108
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=87,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55890805 0.67241379 0.55316092 0.60723514 0.44573643]
----------------------------------------
Trial 2109
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.70545977 0.7183908 0.57622739 0.43927649]
----------------------------------------
Trial 2110
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=149,
random_state=100))])
cv score: [0.64511494 0.64942529 0.47844828 0.66925065 0.39793282]
----------------------------------------
Trial 2111
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
n_estimators=148, random_state=100,
subsample=0.6))])
cv score: [0.57471264 0.71264368 0.53017241 0.47674419 0.48191214]
----------------------------------------
Trial 2112
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=182,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.67385057 0.61063218 0.63049096 0.41860465]
----------------------------------------
Trial 2113
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=53,
random_state=100))])
cv score: [0.70402299 0.63362069 0.66954023 0.60594315 0.36692506]
----------------------------------------
Trial 2114
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='sqrt',
n_estimators=66, random_state=100,
subsample=0.65))])
cv score: [0.72270115 0.63936782 0.56896552 0.62919897 0.40439276]
----------------------------------------
Trial 2115
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=151,
random_state=100))])
cv score: [0.65229885 0.66091954 0.59051724 0.64211886 0.43927649]
----------------------------------------
Trial 2116
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=45, random_state=100,
subsample=0.75))])
cv score: [0.53591954 0.66666667 0.50862069 0.66020672 0.38888889]
----------------------------------------
Trial 2117
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=172, random_state=100))])
cv score: [0.73132184 0.62212644 0.66235632 0.61369509 0.4121447 ]
----------------------------------------
Trial 2118
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='sqrt',
n_estimators=89, random_state=100,
subsample=0.6))])
cv score: [0.6408046 0.61925287 0.57327586 0.60335917 0.45736434]
----------------------------------------
Trial 2119
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=169,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.61925287 0.58333333 0.61757106 0.43023256]
----------------------------------------
Trial 2120
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=19,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75718391 0.62571839 0.66738506 0.60142119 0.44379845]
----------------------------------------
Trial 2121
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=146,
random_state=100))])
cv score: [0.59051724 0.67528736 0.63362069 0.71576227 0.40826873]
----------------------------------------
Trial 2122
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.68965517 0.70402299 0.64728682 0.38888889]
----------------------------------------
Trial 2123
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=185,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 2124
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=85, random_state=100))])
cv score: [0.74712644 0.63362069 0.66235632 0.56589147 0.38242894]
----------------------------------------
Trial 2125
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=112, random_state=100))])
cv score: [0.73132184 0.63936782 0.65517241 0.5749354 0.38113695]
----------------------------------------
Trial 2126
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='sqrt',
n_estimators=121, random_state=100,
subsample=0.75))])
cv score: [0.70977011 0.66091954 0.58045977 0.67054264 0.45607235]
----------------------------------------
Trial 2127
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=164,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.63505747 0.6091954 0.60594315 0.42635659]
----------------------------------------
Trial 2128
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=127, random_state=100,
subsample=0.75))])
cv score: [0.68247126 0.64655172 0.67816092 0.62273902 0.4121447 ]
----------------------------------------
Trial 2129
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=151,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57758621 0.74568966 0.56752874 0.56330749 0.49354005]
----------------------------------------
Trial 2130
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=132, random_state=100,
subsample=0.65))])
cv score: [0.625 0.64511494 0.50718391 0.61498708 0.46124031]
----------------------------------------
Trial 2131
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=136,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.68103448 0.63074713 0.59173127 0.44573643]
----------------------------------------
Trial 2132
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=134,
random_state=100))])
cv score: [0.58189655 0.68606322 0.58189655 0.66666667 0.42118863]
----------------------------------------
Trial 2133
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=12,
random_state=100))])
cv score: [0.70689655 0.56034483 0.44827586 0.59819121 0.36498708]
----------------------------------------
Trial 2134
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=94, random_state=100))])
cv score: [0.7341954 0.6091954 0.58189655 0.58656331 0.42894057]
----------------------------------------
Trial 2135
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=19, random_state=100,
subsample=0.7))])
cv score: [0.39798851 0.49856322 0.48994253 0.67183463 0.63307494]
----------------------------------------
Trial 2136
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=123,
random_state=100))])
cv score: [0.60344828 0.68534483 0.61063218 0.70930233 0.42312661]
----------------------------------------
Trial 2137
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.66810345 0.65804598 0.66149871 0.43410853]
----------------------------------------
Trial 2138
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6795977 0.64942529 0.61637931 0.66666667 0.4005168 ]
----------------------------------------
Trial 2139
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=118,
random_state=100,
subsample=0.75))])
cv score: [0.54022989 0.66666667 0.67385057 0.59043928 0.42118863]
----------------------------------------
Trial 2140
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=40, random_state=100))])
cv score: [0.71408046 0.64367816 0.70689655 0.56589147 0.38113695]
----------------------------------------
Trial 2141
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=136,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.65086207 0.48275862 0.5503876 0.41731266]
----------------------------------------
Trial 2142
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=90,
random_state=100))])
cv score: [0.5862069 0.60344828 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 2143
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=64,
random_state=100))])
cv score: [0.56321839 0.61206897 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 2144
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='log2',
n_estimators=32, random_state=100,
subsample=0.6))])
cv score: [0.65517241 0.63793103 0.60488506 0.58268734 0.4870801 ]
----------------------------------------
Trial 2145
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=143, random_state=100,
subsample=0.6))])
cv score: [0.67385057 0.60201149 0.57327586 0.6369509 0.42764858]
----------------------------------------
Trial 2146
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.70833333 0.63936782 0.61111111 0.39922481]
----------------------------------------
Trial 2147
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=71, random_state=100,
subsample=0.9))])
cv score: [0.72844828 0.6566092 0.65948276 0.60271318 0.37080103]
----------------------------------------
Trial 2148
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, n_estimators=162,
random_state=100, subsample=0.6))])
cv score: [0.63936782 0.6566092 0.47413793 0.66795866 0.42118863]
----------------------------------------
Trial 2149
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=57,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77514368 0.62428161 0.68606322 0.63565891 0.38824289]
----------------------------------------
Trial 2150
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=184, random_state=100,
subsample=0.95))])
cv score: [0.63074713 0.67385057 0.46551724 0.60077519 0.50775194]
----------------------------------------
Trial 2151
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=51,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 2152
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=105,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.69971264 0.50862069 0.62015504 0.41085271]
----------------------------------------
Trial 2153
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
n_estimators=103, random_state=100,
subsample=0.75))])
cv score: [0.5704023 0.7183908 0.54885057 0.7248062 0.44315245]
----------------------------------------
Trial 2154
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=80,
random_state=100))])
cv score: [0.78735632 0.625 0.66307471 0.62919897 0.41989664]
----------------------------------------
Trial 2155
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=189, random_state=100))])
cv score: [0.61637931 0.67672414 0.58045977 0.61886305 0.44056848]
----------------------------------------
Trial 2156
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=57,
random_state=100))])
cv score: [0.63074713 0.68318966 0.61063218 0.69702842 0.44056848]
----------------------------------------
Trial 2157
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=21,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56976744 0.41860465]
----------------------------------------
Trial 2158
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=53,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.68103448 0.69252874 0.62661499 0.40180879]
----------------------------------------
Trial 2159
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66666667 0.62068966 0.70977011 0.59043928 0.40180879]
----------------------------------------
Trial 2160
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=181,
random_state=100))])
cv score: [0.77298851 0.625 0.69324713 0.64341085 0.40956072]
----------------------------------------
Trial 2161
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='sqrt',
n_estimators=85,
random_state=100))])
cv score: [0.65948276 0.75718391 0.56321839 0.61886305 0.47932817]
----------------------------------------
Trial 2162
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=140,
random_state=100))])
cv score: [0.59051724 0.68606322 0.59195402 0.68475452 0.41989664]
----------------------------------------
Trial 2163
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=114, random_state=100))])
cv score: [0.75862069 0.63218391 0.65517241 0.58010336 0.39147287]
----------------------------------------
Trial 2164
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.73563218 0.56896552 0.52971576 0.4496124 ]
----------------------------------------
Trial 2165
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=199, random_state=100))])
cv score: [0.74137931 0.63074713 0.69109195 0.66666667 0.41602067]
----------------------------------------
Trial 2166
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=11,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7816092 0.56681034 0.58979885 0.53294574 0.4748062 ]
----------------------------------------
Trial 2167
----------------------------------------
Parameters {'xgb__n_estimators': 63, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=63,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74928161 0.58045977 0.49928161 0.55167959 0.37080103]
----------------------------------------
Trial 2168
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=88, random_state=100))])
cv score: [0.58908046 0.66235632 0.56609195 0.62273902 0.44832041]
----------------------------------------
Trial 2169
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=66,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73275862 0.70545977 0.55028736 0.5994832 0.41860465]
----------------------------------------
Trial 2170
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=31,
random_state=100))])
cv score: [0.7183908 0.61781609 0.53017241 0.56847545 0.40956072]
----------------------------------------
Trial 2171
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=110,
random_state=100))])
cv score: [0.57183908 0.5941092 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 2172
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=74, random_state=100))])
cv score: [0.64655172 0.63218391 0.47557471 0.67183463 0.39664083]
----------------------------------------
Trial 2173
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68318966 0.70258621 0.72557471 0.6621447 0.4625323 ]
----------------------------------------
Trial 2174
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=191,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.69683908 0.57758621 0.61498708 0.34625323]
----------------------------------------
Trial 2175
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.62643678 0.625 0.62403101 0.41795866]
----------------------------------------
Trial 2176
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71695402 0.65804598 0.51005747 0.58914729 0.34496124]
----------------------------------------
Trial 2177
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=148,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.6566092 0.57327586 0.57751938 0.39018088]
----------------------------------------
Trial 2178
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=75, random_state=100))])
cv score: [0.62931034 0.625 0.61781609 0.63953488 0.37338501]
----------------------------------------
Trial 2179
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=146, random_state=100))])
cv score: [0.61925287 0.70545977 0.67241379 0.67700258 0.42700258]
----------------------------------------
Trial 2180
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=54,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55100575 0.59698276 0.47126437 0.52777778 0.35658915]
----------------------------------------
Trial 2181
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=23,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75718391 0.57974138 0.6795977 0.60400517 0.38824289]
----------------------------------------
Trial 2182
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=156,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.70977011 0.6408046 0.6124031 0.38113695]
----------------------------------------
Trial 2183
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=158,
random_state=100))])
cv score: [0.57327586 0.5941092 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 2184
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=84,
random_state=100))])
cv score: [0.6875 0.63505747 0.51724138 0.5381137 0.36950904]
----------------------------------------
Trial 2185
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56896552 0.67097701 0.69252874 0.5994832 0.40180879]
----------------------------------------
Trial 2186
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=67, random_state=100))])
cv score: [0.71264368 0.65948276 0.66235632 0.58656331 0.38888889]
----------------------------------------
Trial 2187
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=161, random_state=100,
subsample=0.85))])
cv score: [0.77442529 0.61925287 0.72844828 0.63824289 0.35594315]
----------------------------------------
Trial 2188
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.68390805 0.6091954 0.6124031 0.42118863]
----------------------------------------
Trial 2189
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=187, random_state=100,
subsample=0.9))])
cv score: [0.52873563 0.73563218 0.60488506 0.69250646 0.3255814 ]
----------------------------------------
Trial 2190
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=75, random_state=100))])
cv score: [0.59913793 0.65086207 0.53663793 0.59689922 0.43023256]
----------------------------------------
Trial 2191
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=152, random_state=100))])
cv score: [0.64367816 0.6566092 0.62643678 0.68087855 0.39922481]
----------------------------------------
Trial 2192
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=54,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75143678 0.61781609 0.67600575 0.65116279 0.39534884]
----------------------------------------
Trial 2193
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=91,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63793103 0.69109195 0.65948276 0.64211886 0.42377261]
----------------------------------------
Trial 2194
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=108,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 2195
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=16, random_state=100))])
cv score: [0.61135057 0.63362069 0.48204023 0.56395349 0.35788114]
----------------------------------------
Trial 2196
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76149425 0.5933908 0.59554598 0.53617571 0.47351421]
----------------------------------------
Trial 2197
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='sqrt',
n_estimators=41, random_state=100,
subsample=0.6))])
cv score: [0.6954023 0.61350575 0.68534483 0.63953488 0.47674419]
----------------------------------------
Trial 2198
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=124, random_state=100))])
cv score: [0.60488506 0.66954023 0.59482759 0.62919897 0.43281654]
----------------------------------------
Trial 2199
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=141,
random_state=100))])
cv score: [0.64224138 0.66091954 0.59770115 0.64599483 0.4379845 ]
----------------------------------------
Trial 2200
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=175, random_state=100,
subsample=0.9))])
cv score: [0.68247126 0.70833333 0.51005747 0.63049096 0.43023256]
----------------------------------------
Trial 2201
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=94, random_state=100))])
cv score: [0.5941092 0.71336207 0.6408046 0.6505168 0.43992248]
----------------------------------------
Trial 2202
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=191,
random_state=100))])
cv score: [0.61350575 0.65373563 0.61350575 0.71963824 0.41085271]
----------------------------------------
Trial 2203
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=68,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.71264368 0.60057471 0.65374677 0.40568475]
----------------------------------------
Trial 2204
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='sqrt',
n_estimators=73, random_state=100,
subsample=0.85))])
cv score: [0.58333333 0.65229885 0.49568966 0.62144703 0.46511628]
----------------------------------------
Trial 2205
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=176,
random_state=100))])
cv score: [0.55172414 0.63218391 0.5704023 0.7377261 0.4625323 ]
----------------------------------------
Trial 2206
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=50, random_state=100))])
cv score: [0.54885057 0.65948276 0.56465517 0.5878553 0.46059432]
----------------------------------------
Trial 2207
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=126, random_state=100))])
cv score: [0.60201149 0.72198276 0.63218391 0.67635659 0.4250646 ]
----------------------------------------
Trial 2208
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=32,
random_state=100))])
cv score: [0.5933908 0.65373563 0.625 0.56072351 0.43410853]
----------------------------------------
Trial 2209
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='sqrt',
n_estimators=102, random_state=100,
subsample=0.7))])
cv score: [0.57902299 0.6795977 0.59626437 0.61111111 0.46511628]
----------------------------------------
Trial 2210
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=128, random_state=100,
subsample=0.8))])
cv score: [0.60201149 0.72557471 0.58764368 0.70413437 0.42764858]
----------------------------------------
Trial 2211
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=142,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72701149 0.58477011 0.63074713 0.56912145 0.38565891]
----------------------------------------
Trial 2212
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.64798851 0.55603448 0.6873385 0.46124031]
----------------------------------------
Trial 2213
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=67, random_state=100))])
cv score: [0.73275862 0.63218391 0.70833333 0.5620155 0.37338501]
----------------------------------------
Trial 2214
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=82,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 2215
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='sqrt',
n_estimators=64, random_state=100,
subsample=0.9))])
cv score: [0.66091954 0.65948276 0.59626437 0.52583979 0.45994832]
----------------------------------------
Trial 2216
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=92,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.63505747 0.63218391 0.64082687 0.38501292]
----------------------------------------
Trial 2217
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68390805 0.63649425 0.7183908 0.62984496 0.39211886]
----------------------------------------
Trial 2218
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=104,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56609195 0.67816092 0.70114943 0.6369509 0.39664083]
----------------------------------------
Trial 2219
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=140,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.64224138 0.63362069 0.55684755 0.35788114]
----------------------------------------
Trial 2220
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
n_estimators=46, random_state=100,
subsample=0.9))])
cv score: [0.63936782 0.68965517 0.63505747 0.69509044 0.39405685]
----------------------------------------
Trial 2221
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=140,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56178161 0.67672414 0.54885057 0.61111111 0.42118863]
----------------------------------------
Trial 2222
----------------------------------------
Parameters {'xgb__n_estimators': 63, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=63,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51867816 0.7112069 0.5862069 0.61369509 0.43023256]
----------------------------------------
Trial 2223
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=139, random_state=100,
subsample=0.75))])
cv score: [0.63793103 0.6637931 0.4066092 0.61369509 0.45478036]
----------------------------------------
Trial 2224
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=23, random_state=100))])
cv score: [0.54956897 0.59698276 0.45761494 0.5994832 0.5497416 ]
----------------------------------------
Trial 2225
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=35, random_state=100,
subsample=0.7))])
cv score: [0.7341954 0.59626437 0.68534483 0.65633075 0.42183463]
----------------------------------------
Trial 2226
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=24, random_state=100))])
cv score: [0.60057471 0.63505747 0.5158046 0.59431525 0.54134367]
----------------------------------------
Trial 2227
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
n_estimators=199, random_state=100,
subsample=0.95))])
cv score: [0.51293103 0.65948276 0.57183908 0.67829457 0.38501292]
----------------------------------------
Trial 2228
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=110, random_state=100))])
cv score: [0.74712644 0.65229885 0.64224138 0.63953488 0.43152455]
----------------------------------------
Trial 2229
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=129,
random_state=100))])
cv score: [0.67816092 0.68821839 0.61206897 0.63049096 0.44573643]
----------------------------------------
Trial 2230
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.68965517 0.62212644 0.56330749 0.39405685]
----------------------------------------
Trial 2231
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
n_estimators=116, random_state=100,
subsample=0.6))])
cv score: [0.66091954 0.68247126 0.57327586 0.70155039 0.42894057]
----------------------------------------
Trial 2232
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=189,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77873563 0.66666667 0.68821839 0.625323 0.40503876]
----------------------------------------
Trial 2233
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=135,
random_state=100))])
cv score: [0.77873563 0.64224138 0.67887931 0.6369509 0.40310078]
----------------------------------------
Trial 2234
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80028736 0.5933908 0.67816092 0.58979328 0.41925065]
----------------------------------------
Trial 2235
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=145, random_state=100,
subsample=0.9))])
cv score: [0.54885057 0.73275862 0.56609195 0.65116279 0.32428941]
----------------------------------------
Trial 2236
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=50, random_state=100,
subsample=0.9))])
cv score: [0.52298851 0.73275862 0.46264368 0.67700258 0.40180879]
----------------------------------------
Trial 2237
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=87,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6795977 0.65086207 0.6795977 0.5503876 0.42894057]
----------------------------------------
Trial 2238
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=166, random_state=100))])
cv score: [0.61206897 0.70114943 0.66235632 0.69250646 0.44186047]
----------------------------------------
Trial 2239
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=83, random_state=100,
subsample=0.85))])
cv score: [0.59482759 0.63362069 0.4612069 0.50645995 0.50775194]
----------------------------------------
Trial 2240
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='sqrt',
n_estimators=85, random_state=100,
subsample=0.6))])
cv score: [0.66810345 0.57183908 0.44971264 0.58914729 0.46511628]
----------------------------------------
Trial 2241
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=140,
random_state=100))])
cv score: [0.59051724 0.68606322 0.59195402 0.68475452 0.41989664]
----------------------------------------
Trial 2242
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=130, random_state=100,
subsample=0.75))])
cv score: [0.63936782 0.61206897 0.5933908 0.63436693 0.46511628]
----------------------------------------
Trial 2243
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.71551724 0.53448276 0.6124031 0.51162791]
----------------------------------------
Trial 2244
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=49,
random_state=100, subsample=0.7))])
cv score: [0.55747126 0.73132184 0.52155172 0.72093023 0.44444444]
----------------------------------------
Trial 2245
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=106, random_state=100,
subsample=0.75))])
cv score: [0.68678161 0.40517241 0.46264368 0.61757106 0.44702842]
----------------------------------------
Trial 2246
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=46, random_state=100))])
cv score: [0.58189655 0.64583333 0.56106322 0.5994832 0.44638243]
----------------------------------------
Trial 2247
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=57,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.62643678 0.67385057 0.66925065 0.45994832]
----------------------------------------
Trial 2248
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=180, random_state=100,
subsample=0.7))])
cv score: [0.63793103 0.69252874 0.5316092 0.71059432 0.49870801]
----------------------------------------
Trial 2249
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=37, random_state=100))])
cv score: [0.60201149 0.64942529 0.61063218 0.57364341 0.49354005]
----------------------------------------
Trial 2250
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=114, random_state=100,
subsample=0.95))])
cv score: [0.61206897 0.65086207 0.52586207 0.60852713 0.44444444]
----------------------------------------
Trial 2251
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74066092 0.55603448 0.38146552 0.57041344 0.45284238]
----------------------------------------
Trial 2252
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78376437 0.57902299 0.72054598 0.59883721 0.40245478]
----------------------------------------
Trial 2253
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=132, random_state=100))])
cv score: [0.67672414 0.68678161 0.59482759 0.6498708 0.41602067]
----------------------------------------
Trial 2254
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=24, random_state=100))])
cv score: [0.68965517 0.69396552 0.5545977 0.5245478 0.39470284]
----------------------------------------
Trial 2255
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=37,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67816092 0.60775862 0.64224138 0.60206718 0.39534884]
----------------------------------------
Trial 2256
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=139,
random_state=100))])
cv score: [0.59195402 0.67385057 0.64655172 0.68087855 0.42764858]
----------------------------------------
Trial 2257
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='sqrt',
n_estimators=160, random_state=100,
subsample=0.6))])
cv score: [0.58692529 0.48994253 0.56752874 0.374677 0.50258398]
----------------------------------------
Trial 2258
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71695402 0.57686782 0.63936782 0.60723514 0.44702842]
----------------------------------------
Trial 2259
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=144,
random_state=100))])
cv score: [0.59051724 0.67097701 0.64224138 0.68217054 0.42894057]
----------------------------------------
Trial 2260
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=158, random_state=100,
subsample=0.75))])
cv score: [0.63649425 0.66091954 0.39798851 0.64599483 0.45736434]
----------------------------------------
Trial 2261
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=45,
random_state=100))])
cv score: [0.6795977 0.6170977 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 2262
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.70545977 0.51724138 0.62144703 0.40956072]
----------------------------------------
Trial 2263
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=102, random_state=100))])
cv score: [0.62787356 0.6795977 0.6091954 0.65633075 0.41731266]
----------------------------------------
Trial 2264
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=189,
random_state=100))])
cv score: [0.66235632 0.64224138 0.62787356 0.65374677 0.44702842]
----------------------------------------
Trial 2265
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=102, random_state=100))])
cv score: [0.61422414 0.70617816 0.64224138 0.66602067 0.4244186 ]
----------------------------------------
Trial 2266
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=122, random_state=100,
subsample=0.85))])
cv score: [0.68247126 0.71982759 0.54741379 0.63824289 0.49483204]
----------------------------------------
Trial 2267
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='sqrt',
n_estimators=137, random_state=100,
subsample=0.95))])
cv score: [0.7112069 0.67241379 0.58764368 0.6744186 0.43023256]
----------------------------------------
Trial 2268
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=158, random_state=100))])
cv score: [0.59913793 0.70617816 0.66954023 0.70671835 0.42635659]
----------------------------------------
Trial 2269
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
n_estimators=115, random_state=100,
subsample=0.75))])
cv score: [0.54885057 0.70402299 0.58045977 0.70155039 0.47157623]
----------------------------------------
Trial 2270
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=71,
random_state=100))])
cv score: [0.53448276 0.61781609 0.5316092 0.66795866 0.40956072]
----------------------------------------
Trial 2271
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=126,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74281609 0.60201149 0.65373563 0.60594315 0.41472868]
----------------------------------------
Trial 2272
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.75431034 0.65086207 0.60852713 0.41343669]
----------------------------------------
Trial 2273
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=53,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64942529 0.69252874 0.54741379 0.55297158 0.50775194]
----------------------------------------
Trial 2274
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=54, random_state=100,
subsample=0.6))])
cv score: [0.65948276 0.61925287 0.60775862 0.56976744 0.46770026]
----------------------------------------
Trial 2275
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=35,
random_state=100))])
cv score: [0.60775862 0.66594828 0.64798851 0.62080103 0.39922481]
----------------------------------------
Trial 2276
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=180, random_state=100,
subsample=0.7))])
cv score: [0.52873563 0.6091954 0.56034483 0.70413437 0.44186047]
----------------------------------------
Trial 2277
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=18, random_state=100))])
cv score: [0.61781609 0.64942529 0.71695402 0.53165375 0.46770026]
----------------------------------------
Trial 2278
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=27, random_state=100))])
cv score: [0.59482759 0.62931034 0.5 0.72093023 0.36563307]
----------------------------------------
Trial 2279
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=176, random_state=100,
subsample=0.6))])
cv score: [0.47413793 0.59051724 0.61494253 0.58268734 0.42377261]
----------------------------------------
Trial 2280
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=167,
random_state=100, subsample=0.9))])
cv score: [0.59195402 0.64511494 0.51724138 0.69379845 0.47803618]
----------------------------------------
Trial 2281
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=169,
random_state=100))])
cv score: [0.54454023 0.65876437 0.58189655 0.73901809 0.41666667]
----------------------------------------
Trial 2282
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
n_estimators=191, random_state=100,
subsample=0.7))])
cv score: [0.59770115 0.68678161 0.57183908 0.57105943 0.49483204]
----------------------------------------
Trial 2283
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.66810345 0.58477011 0.48320413 0.45994832]
----------------------------------------
Trial 2284
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=195,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75933908 0.59123563 0.67456897 0.56589147 0.40568475]
----------------------------------------
Trial 2285
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=48,
random_state=100))])
cv score: [0.61206897 0.60416667 0.57686782 0.57105943 0.45348837]
----------------------------------------
Trial 2286
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=46,
random_state=100))])
cv score: [0.65804598 0.62787356 0.6637931 0.60206718 0.43669251]
----------------------------------------
Trial 2287
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=36, random_state=100))])
cv score: [0.67816092 0.67313218 0.61925287 0.65762274 0.46382429]
----------------------------------------
Trial 2288
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=189,
random_state=100))])
cv score: [0.5933908 0.67241379 0.62643678 0.69896641 0.39534884]
----------------------------------------
Trial 2289
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=37, random_state=100))])
cv score: [0.56178161 0.69612069 0.56681034 0.62855297 0.50322997]
----------------------------------------
Trial 2290
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=196, random_state=100,
subsample=0.75))])
cv score: [0.61350575 0.64798851 0.5 0.66537468 0.47028424]
----------------------------------------
Trial 2291
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='sqrt',
n_estimators=140, random_state=100,
subsample=0.75))])
cv score: [0.66522989 0.68103448 0.62068966 0.62661499 0.4250646 ]
----------------------------------------
Trial 2292
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=188,
random_state=100))])
cv score: [0.77586207 0.62931034 0.6875 0.64211886 0.40956072]
----------------------------------------
Trial 2293
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='sqrt',
n_estimators=14, random_state=100,
subsample=0.75))])
cv score: [0.46408046 0.65804598 0.60632184 0.73126615 0.48449612]
----------------------------------------
Trial 2294
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
n_estimators=163, random_state=100,
subsample=0.6))])
cv score: [0.61637931 0.70402299 0.57327586 0.65503876 0.48449612]
----------------------------------------
Trial 2295
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=19,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.69827586 0.72844828 0.62919897 0.48385013]
----------------------------------------
Trial 2296
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
n_estimators=102, random_state=100,
subsample=0.65))])
cv score: [0.68678161 0.67097701 0.58045977 0.68992248 0.54780362]
----------------------------------------
Trial 2297
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=182,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69109195 0.66091954 0.65804598 0.62015504 0.44186047]
----------------------------------------
Trial 2298
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=55,
random_state=100))])
cv score: [0.63649425 0.60272989 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 2299
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=177, random_state=100))])
cv score: [0.73706897 0.62356322 0.66091954 0.60852713 0.40697674]
----------------------------------------
Trial 2300
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=22, random_state=100))])
cv score: [0.60488506 0.63362069 0.71408046 0.52648579 0.44315245]
----------------------------------------
Trial 2301
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
n_estimators=176, random_state=100,
subsample=0.65))])
cv score: [0.62068966 0.6954023 0.65948276 0.69379845 0.42764858]
----------------------------------------
Trial 2302
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=108,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.67097701 0.57471264 0.60206718 0.39405685]
----------------------------------------
Trial 2303
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.9))])
cv score: [0.61781609 0.70114943 0.56034483 0.67312661 0.48320413]
----------------------------------------
Trial 2304
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=182,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.68821839 0.60775862 0.53488372 0.41085271]
----------------------------------------
Trial 2305
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=84,
random_state=100))])
cv score: [0.55747126 0.67385057 0.63793103 0.72093023 0.34819121]
----------------------------------------
Trial 2306
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.69396552 0.63649425 0.61111111 0.35658915]
----------------------------------------
Trial 2307
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=129, random_state=100,
subsample=0.9))])
cv score: [0.57327586 0.69827586 0.53591954 0.62273902 0.42894057]
----------------------------------------
Trial 2308
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=177,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66666667 0.66666667 0.6795977 0.6369509 0.43023256]
----------------------------------------
Trial 2309
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=186,
random_state=100))])
cv score: [0.54741379 0.65373563 0.59770115 0.74160207 0.40374677]
----------------------------------------
Trial 2310
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=70, random_state=100,
subsample=0.75))])
cv score: [0.62931034 0.66522989 0.49425287 0.57364341 0.43281654]
----------------------------------------
Trial 2311
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=195,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.68534483 0.63505747 0.67700258 0.47803618]
----------------------------------------
Trial 2312
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
n_estimators=64, random_state=100,
subsample=0.8))])
cv score: [0.57758621 0.76436782 0.58045977 0.69250646 0.41085271]
----------------------------------------
Trial 2313
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=22,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.64655172 0.64942529 0.63436693 0.36498708]
----------------------------------------
Trial 2314
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72557471 0.65373563 0.6566092 0.60335917 0.38888889]
----------------------------------------
Trial 2315
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=102,
random_state=100))])
cv score: [0.61278736 0.68031609 0.63649425 0.70607235 0.38436693]
----------------------------------------
Trial 2316
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=132, random_state=100))])
cv score: [0.61350575 0.72557471 0.65086207 0.68152455 0.40633075]
----------------------------------------
Trial 2317
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=83, random_state=100))])
cv score: [0.68390805 0.68390805 0.60488506 0.63953488 0.41602067]
----------------------------------------
Trial 2318
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=124, random_state=100,
subsample=0.6))])
cv score: [0.58764368 0.67097701 0.53017241 0.62273902 0.38501292]
----------------------------------------
Trial 2319
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=122,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77729885 0.63074713 0.64295977 0.62015504 0.38436693]
----------------------------------------
Trial 2320
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=145, random_state=100))])
cv score: [0.60057471 0.67385057 0.59051724 0.63178295 0.45348837]
----------------------------------------
Trial 2321
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=136,
random_state=100))])
cv score: [0.68031609 0.57399425 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 2322
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=188, random_state=100,
subsample=0.6))])
cv score: [0.62212644 0.66091954 0.5 0.64599483 0.44315245]
----------------------------------------
Trial 2323
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=50, random_state=100))])
cv score: [0.70977011 0.68821839 0.63936782 0.63953488 0.4870801 ]
----------------------------------------
Trial 2324
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=191,
random_state=100))])
cv score: [0.54885057 0.65517241 0.58764368 0.73643411 0.40310078]
----------------------------------------
Trial 2325
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='sqrt',
n_estimators=152, random_state=100,
subsample=0.7))])
cv score: [0.70114943 0.69252874 0.5387931 0.63307494 0.43669251]
----------------------------------------
Trial 2326
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=84,
random_state=100))])
cv score: [0.62643678 0.63218391 0.58333333 0.64341085 0.39534884]
----------------------------------------
Trial 2327
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=144, random_state=100))])
cv score: [0.71982759 0.62068966 0.65229885 0.60981912 0.41989664]
----------------------------------------
Trial 2328
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=190,
random_state=100))])
cv score: [0.63721264 0.6012931 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 2329
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=144, random_state=100,
subsample=0.7))])
cv score: [0.56034483 0.69827586 0.52586207 0.69379845 0.47286822]
----------------------------------------
Trial 2330
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76867816 0.57471264 0.50215517 0.52777778 0.36627907]
----------------------------------------
Trial 2331
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=161, random_state=100,
subsample=0.9))])
cv score: [0.68678161 0.64367816 0.49281609 0.61369509 0.52971576]
----------------------------------------
Trial 2332
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=162,
random_state=100, subsample=0.9))])
cv score: [0.56034483 0.74712644 0.62068966 0.60206718 0.4121447 ]
----------------------------------------
Trial 2333
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='log2',
n_estimators=183, random_state=100,
subsample=0.75))])
cv score: [0.62212644 0.69396552 0.59626437 0.61627907 0.52196382]
----------------------------------------
Trial 2334
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=40, random_state=100,
subsample=0.65))])
cv score: [0.4683908 0.54741379 0.63362069 0.67571059 0.52971576]
----------------------------------------
Trial 2335
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='sqrt',
n_estimators=108, random_state=100,
subsample=0.8))])
cv score: [0.6566092 0.67241379 0.55316092 0.44186047 0.40697674]
----------------------------------------
Trial 2336
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=196, random_state=100,
subsample=0.75))])
cv score: [0.59051724 0.7112069 0.5933908 0.67183463 0.39534884]
----------------------------------------
Trial 2337
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=117,
random_state=100, subsample=0.8))])
cv score: [0.59195402 0.70977011 0.5704023 0.67958656 0.44444444]
----------------------------------------
Trial 2338
----------------------------------------
Parameters {'rf__n_estimators': 17, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=17,
random_state=100))])
cv score: [0.68103448 0.62787356 0.55172414 0.69121447 0.37855297]
----------------------------------------
Trial 2339
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64942529 0.66522989 0.7112069 0.65891473 0.40956072]
----------------------------------------
Trial 2340
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=123, random_state=100))])
cv score: [0.57183908 0.66235632 0.59626437 0.63307494 0.43669251]
----------------------------------------
Trial 2341
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=21, random_state=100))])
cv score: [0.6637931 0.63074713 0.41091954 0.60658915 0.41731266]
----------------------------------------
Trial 2342
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=149, random_state=100,
subsample=0.7))])
cv score: [0.56896552 0.69827586 0.54741379 0.68346253 0.42894057]
----------------------------------------
Trial 2343
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=45, random_state=100))])
cv score: [0.57686782 0.71264368 0.62356322 0.70994832 0.42248062]
----------------------------------------
Trial 2344
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67025862 0.57112069 0.63505747 0.5626615 0.38372093]
----------------------------------------
Trial 2345
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=121,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7816092 0.57112069 0.68390805 0.55426357 0.4250646 ]
----------------------------------------
Trial 2346
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=61,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61781609 0.69827586 0.68678161 0.60206718 0.47416021]
----------------------------------------
Trial 2347
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=182,
random_state=100))])
cv score: [0.56034483 0.6954023 0.42672414 0.58914729 0.46770026]
----------------------------------------
Trial 2348
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
n_estimators=108, random_state=100,
subsample=0.95))])
cv score: [0.54454023 0.70545977 0.57327586 0.75839793 0.4379845 ]
----------------------------------------
Trial 2349
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=193,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.72557471 0.61494253 0.60206718 0.39922481]
----------------------------------------
Trial 2350
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=131, random_state=100))])
cv score: [0.72557471 0.60344828 0.6637931 0.60335917 0.41602067]
----------------------------------------
Trial 2351
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=136, random_state=100))])
cv score: [0.57183908 0.68175287 0.61637931 0.60206718 0.44702842]
----------------------------------------
Trial 2352
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='log2',
n_estimators=69, random_state=100,
subsample=0.65))])
cv score: [0.65804598 0.67528736 0.62068966 0.63953488 0.47674419]
----------------------------------------
Trial 2353
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=197, random_state=100))])
cv score: [0.64224138 0.66954023 0.57902299 0.68087855 0.38501292]
----------------------------------------
Trial 2354
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77011494 0.65086207 0.67816092 0.64922481 0.45219638]
----------------------------------------
Trial 2355
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=162, random_state=100,
subsample=0.95))])
cv score: [0.57327586 0.57327586 0.4841954 0.70413437 0.43023256]
----------------------------------------
Trial 2356
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77945402 0.64367816 0.625 0.62015504 0.44444444]
----------------------------------------
Trial 2357
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=167,
random_state=100))])
cv score: [0.77298851 0.62356322 0.68606322 0.65503876 0.40697674]
----------------------------------------
Trial 2358
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51724138 0.70114943 0.75143678 0.65633075 0.40762274]
----------------------------------------
Trial 2359
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=93,
random_state=100))])
cv score: [0.58045977 0.69109195 0.46695402 0.69638243 0.42118863]
----------------------------------------
Trial 2360
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=58,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.64224138 0.72557471 0.5994832 0.39276486]
----------------------------------------
Trial 2361
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=10, random_state=100,
subsample=0.6))])
cv score: [0.83405172 0.625 0.73204023 0.70994832 0.41602067]
----------------------------------------
Trial 2362
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.65))])
cv score: [0.75718391 0.64942529 0.69971264 0.60852713 0.52713178]
----------------------------------------
Trial 2363
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
n_estimators=157, random_state=100,
subsample=0.75))])
cv score: [0.58908046 0.63649425 0.50143678 0.60723514 0.40956072]
----------------------------------------
Trial 2364
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=22,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.65804598 0.55747126 0.6369509 0.48966408]
----------------------------------------
Trial 2365
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=156,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.72701149 0.50862069 0.49870801 0.4754522 ]
----------------------------------------
Trial 2366
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=102,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.64367816 0.62356322 0.56718346 0.46382429]
----------------------------------------
Trial 2367
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=51,
random_state=100))])
cv score: [0.74568966 0.63505747 0.7112069 0.64599483 0.41085271]
----------------------------------------
Trial 2368
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=198,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.6566092 0.64798851 0.6744186 0.40826873]
----------------------------------------
Trial 2369
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=109, random_state=100,
subsample=0.9))])
cv score: [0.59051724 0.7341954 0.54741379 0.62015504 0.43540052]
----------------------------------------
Trial 2370
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=114,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73132184 0.57327586 0.6487069 0.5503876 0.39793282]
----------------------------------------
Trial 2371
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=130, random_state=100))])
cv score: [0.57327586 0.69181034 0.61278736 0.60981912 0.43475452]
----------------------------------------
Trial 2372
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=155,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.7183908 0.7341954 0.63178295 0.38113695]
----------------------------------------
Trial 2373
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=78, random_state=100))])
cv score: [0.68821839 0.68247126 0.60201149 0.62661499 0.41731266]
----------------------------------------
Trial 2374
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7966954 0.55100575 0.64511494 0.64793282 0.3624031 ]
----------------------------------------
Trial 2375
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68534483 0.70258621 0.65229885 0.5755814 0.36627907]
----------------------------------------
Trial 2376
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=14, random_state=100))])
cv score: [0.50574713 0.70402299 0.55603448 0.65633075 0.48837209]
----------------------------------------
Trial 2377
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=87,
random_state=100))])
cv score: [0.57471264 0.66091954 0.55890805 0.65891473 0.39405685]
----------------------------------------
Trial 2378
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=146, random_state=100))])
cv score: [0.73563218 0.64367816 0.66954023 0.63953488 0.41343669]
----------------------------------------
Trial 2379
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=186, random_state=100))])
cv score: [0.72126437 0.63793103 0.64224138 0.59431525 0.40568475]
----------------------------------------
Trial 2380
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=99,
random_state=100))])
cv score: [0.7112069 0.64798851 0.6795977 0.65245478 0.40439276]
----------------------------------------
Trial 2381
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=54, random_state=100,
subsample=0.75))])
cv score: [0.5862069 0.66091954 0.45545977 0.68087855 0.36046512]
----------------------------------------
Trial 2382
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=102,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70977011 0.69109195 0.68103448 0.59043928 0.4373385 ]
----------------------------------------
Trial 2383
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=80, random_state=100))])
cv score: [0.61494253 0.6408046 0.61063218 0.625323 0.39793282]
----------------------------------------
Trial 2384
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='log2',
n_estimators=59, random_state=100,
subsample=0.85))])
cv score: [0.65086207 0.67385057 0.44971264 0.71576227 0.51162791]
----------------------------------------
Trial 2385
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=132,
random_state=100))])
cv score: [0.63936782 0.65948276 0.58908046 0.64211886 0.43540052]
----------------------------------------
Trial 2386
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.70545977 0.57183908 0.5620155 0.43152455]
----------------------------------------
Trial 2387
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=136, random_state=100,
subsample=0.9))])
cv score: [0.66235632 0.66235632 0.62787356 0.63178295 0.39664083]
----------------------------------------
Trial 2388
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=178, random_state=100))])
cv score: [0.64224138 0.66235632 0.60488506 0.61369509 0.46124031]
----------------------------------------
Trial 2389
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='log2',
n_estimators=49, random_state=100,
subsample=0.7))])
cv score: [0.66522989 0.45402299 0.63362069 0.59689922 0.38888889]
----------------------------------------
Trial 2390
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77586207 0.71695402 0.67816092 0.59043928 0.43410853]
----------------------------------------
Trial 2391
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=14, random_state=100,
subsample=0.9))])
cv score: [0.59195402 0.65804598 0.58908046 0.59431525 0.38501292]
----------------------------------------
Trial 2392
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=52, random_state=100,
subsample=0.65))])
cv score: [0.56321839 0.58908046 0.60201149 0.62015504 0.50516796]
----------------------------------------
Trial 2393
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=25, random_state=100,
subsample=0.6))])
cv score: [0.56896552 0.63936782 0.49281609 0.66537468 0.43152455]
----------------------------------------
Trial 2394
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='sqrt',
n_estimators=71, random_state=100,
subsample=0.65))])
cv score: [0.6408046 0.64224138 0.60488506 0.56976744 0.51162791]
----------------------------------------
Trial 2395
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.95))])
cv score: [0.65086207 0.67097701 0.46408046 0.62144703 0.50258398]
----------------------------------------
Trial 2396
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=190,
random_state=100))])
cv score: [0.60344828 0.6795977 0.56465517 0.67829457 0.43023256]
----------------------------------------
Trial 2397
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.67528736 0.61637931 0.66666667 0.44444444]
----------------------------------------
Trial 2398
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=78, random_state=100,
subsample=0.75))])
cv score: [0.73994253 0.67385057 0.65517241 0.58914729 0.42635659]
----------------------------------------
Trial 2399
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=145,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64367816 0.65229885 0.60632184 0.61111111 0.40826873]
----------------------------------------
Trial 2400
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=109,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67025862 0.59482759 0.59913793 0.54005168 0.38049096]
----------------------------------------
Trial 2401
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=73, random_state=100,
subsample=0.6))])
cv score: [0.63649425 0.54597701 0.54741379 0.53617571 0.2881137 ]
----------------------------------------
Trial 2402
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=175, random_state=100,
subsample=0.85))])
cv score: [0.64655172 0.68821839 0.58045977 0.67829457 0.43023256]
----------------------------------------
Trial 2403
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.8))])
cv score: [0.57758621 0.66810345 0.70258621 0.62273902 0.33850129]
----------------------------------------
Trial 2404
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=94, random_state=100,
subsample=0.85))])
cv score: [0.625 0.64942529 0.61206897 0.67312661 0.44573643]
----------------------------------------
Trial 2405
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=49, random_state=100,
subsample=0.9))])
cv score: [0.67097701 0.56465517 0.72126437 0.64341085 0.46124031]
----------------------------------------
Trial 2406
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
random_state=100))])
cv score: [0.75574713 0.64655172 0.76436782 0.64534884 0.38630491]
----------------------------------------
Trial 2407
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=151, random_state=100,
subsample=0.8))])
cv score: [0.5704023 0.6954023 0.54166667 0.71963824 0.41602067]
----------------------------------------
Trial 2408
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=91,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72270115 0.66307471 0.70186782 0.68023256 0.38307494]
----------------------------------------
Trial 2409
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=69, random_state=100,
subsample=0.6))])
cv score: [0.61206897 0.59195402 0.55890805 0.63436693 0.37984496]
----------------------------------------
Trial 2410
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=21,
random_state=100))])
cv score: [0.55172414 0.74281609 0.52729885 0.63565891 0.40697674]
----------------------------------------
Trial 2411
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=84,
random_state=100))])
cv score: [0.53591954 0.64727011 0.55028736 0.47674419 0.30813953]
----------------------------------------
Trial 2412
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=81,
random_state=100))])
cv score: [0.60344828 0.67816092 0.59482759 0.71059432 0.37596899]
----------------------------------------
Trial 2413
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=156,
random_state=100))])
cv score: [0.57327586 0.5941092 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 2414
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='log2',
n_estimators=172, random_state=100,
subsample=0.8))])
cv score: [0.58764368 0.56034483 0.51005747 0.59560724 0.49483204]
----------------------------------------
Trial 2415
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=40,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 2416
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=61, random_state=100))])
cv score: [0.57686782 0.71408046 0.64727011 0.69379845 0.44573643]
----------------------------------------
Trial 2417
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65373563 0.73994253 0.62212644 0.56330749 0.43669251]
----------------------------------------
Trial 2418
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=113, random_state=100))])
cv score: [0.76005747 0.63362069 0.65804598 0.58397933 0.39276486]
----------------------------------------
Trial 2419
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=39, random_state=100,
subsample=0.9))])
cv score: [0.66810345 0.63074713 0.44252874 0.63307494 0.44832041]
----------------------------------------
Trial 2420
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
n_estimators=37, random_state=100,
subsample=0.6))])
cv score: [0.6408046 0.70545977 0.60488506 0.67571059 0.41085271]
----------------------------------------
Trial 2421
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=47,
random_state=100))])
cv score: [0.41666667 0.68390805 0.45977011 0.68863049 0.55943152]
----------------------------------------
Trial 2422
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=143,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71982759 0.6795977 0.66522989 0.61757106 0.45090439]
----------------------------------------
Trial 2423
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.75574713 0.61063218 0.60594315 0.4496124 ]
----------------------------------------
Trial 2424
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.74712644 0.5704023 0.61627907 0.43669251]
----------------------------------------
Trial 2425
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=55,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5704023 0.60775862 0.54310345 0.63436693 0.38888889]
----------------------------------------
Trial 2426
----------------------------------------
Parameters {'rf__n_estimators': 125, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=125,
random_state=100))])
cv score: [0.68031609 0.57543103 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 2427
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=54, random_state=100,
subsample=0.85))])
cv score: [0.71551724 0.67672414 0.68462644 0.64405685 0.4244186 ]
----------------------------------------
Trial 2428
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=103, random_state=100,
subsample=0.85))])
cv score: [0.60632184 0.70114943 0.57183908 0.5749354 0.374677 ]
----------------------------------------
Trial 2429
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
n_estimators=154, random_state=100,
subsample=0.9))])
cv score: [0.5158046 0.68534483 0.56609195 0.75710594 0.40826873]
----------------------------------------
Trial 2430
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=193,
random_state=100))])
cv score: [0.61637931 0.59985632 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 2431
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=142,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79741379 0.59482759 0.68318966 0.6001292 0.38953488]
----------------------------------------
Trial 2432
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=97, random_state=100))])
cv score: [0.56178161 0.68606322 0.59123563 0.59237726 0.45284238]
----------------------------------------
Trial 2433
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=93, random_state=100))])
cv score: [0.62212644 0.67528736 0.62212644 0.66537468 0.41731266]
----------------------------------------
Trial 2434
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='log2',
n_estimators=65, random_state=100,
subsample=0.65))])
cv score: [0.64367816 0.66522989 0.50862069 0.63436693 0.42118863]
----------------------------------------
Trial 2435
----------------------------------------
Parameters {'xgb__n_estimators': 160, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=160,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73706897 0.67241379 0.67672414 0.63436693 0.45090439]
----------------------------------------
Trial 2436
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56609195 0.70689655 0.75287356 0.60400517 0.38436693]
----------------------------------------
Trial 2437
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=168, random_state=100,
subsample=0.75))])
cv score: [0.60344828 0.67816092 0.54310345 0.63178295 0.41472868]
----------------------------------------
Trial 2438
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=81, random_state=100))])
cv score: [0.57686782 0.71551724 0.64942529 0.6627907 0.42118863]
----------------------------------------
Trial 2439
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=89,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64367816 0.64367816 0.61637931 0.60206718 0.45865633]
----------------------------------------
Trial 2440
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=76, random_state=100,
subsample=0.85))])
cv score: [0.75431034 0.61637931 0.6637931 0.67829457 0.40439276]
----------------------------------------
Trial 2441
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=49, random_state=100,
subsample=0.85))])
cv score: [0.68965517 0.64655172 0.51149425 0.73901809 0.50516796]
----------------------------------------
Trial 2442
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.66810345 0.64942529 0.62273902 0.43410853]
----------------------------------------
Trial 2443
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.66091954 0.64511494 0.65633075 0.36692506]
----------------------------------------
Trial 2444
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=86, random_state=100,
subsample=0.65))])
cv score: [0.74425287 0.59913793 0.69396552 0.59431525 0.39599483]
----------------------------------------
Trial 2445
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='sqrt',
n_estimators=182, random_state=100,
subsample=0.6))])
cv score: [0.61206897 0.68103448 0.56896552 0.63307494 0.4625323 ]
----------------------------------------
Trial 2446
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=155, random_state=100,
subsample=0.8))])
cv score: [0.57758621 0.43678161 0.52586207 0.69896641 0.38888889]
----------------------------------------
Trial 2447
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=48,
random_state=100))])
cv score: [0.59626437 0.63864943 0.65373563 0.6750646 0.4121447 ]
----------------------------------------
Trial 2448
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=76,
random_state=100))])
cv score: [0.60775862 0.68534483 0.59913793 0.70801034 0.39793282]
----------------------------------------
Trial 2449
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=173,
random_state=100, subsample=0.9))])
cv score: [0.52442529 0.72701149 0.55747126 0.64470284 0.36434109]
----------------------------------------
Trial 2450
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=103,
random_state=100))])
cv score: [0.70977011 0.64798851 0.67241379 0.65374677 0.40826873]
----------------------------------------
Trial 2451
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='log2',
n_estimators=99, random_state=100,
subsample=0.6))])
cv score: [0.60344828 0.66091954 0.50287356 0.63565891 0.39922481]
----------------------------------------
Trial 2452
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=54, random_state=100,
subsample=0.85))])
cv score: [0.55316092 0.58908046 0.52586207 0.64728682 0.44573643]
----------------------------------------
Trial 2453
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=151,
random_state=100))])
cv score: [0.72413793 0.64655172 0.57902299 0.625323 0.4250646 ]
----------------------------------------
Trial 2454
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=12,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 2455
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
random_state=100))])
cv score: [0.62212644 0.68821839 0.72126437 0.62919897 0.40310078]
----------------------------------------
Trial 2456
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.72844828 0.59195402 0.56718346 0.41731266]
----------------------------------------
Trial 2457
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=18, random_state=100,
subsample=0.8))])
cv score: [0.76149425 0.56752874 0.66091954 0.65245478 0.4250646 ]
----------------------------------------
Trial 2458
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.67816092 0.67097701 0.63824289 0.45219638]
----------------------------------------
Trial 2459
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
n_estimators=75,
random_state=100))])
cv score: [0.66163793 0.5308908 0.56178161 0.68152455 0.40116279]
----------------------------------------
Trial 2460
----------------------------------------
Parameters {'xgb__n_estimators': 160, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=160,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74856322 0.64224138 0.65229885 0.62855297 0.48643411]
----------------------------------------
Trial 2461
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=113,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.70402299 0.62787356 0.59819121 0.38888889]
----------------------------------------
Trial 2462
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=144, random_state=100))])
cv score: [0.63505747 0.68534483 0.61494253 0.68475452 0.41989664]
----------------------------------------
Trial 2463
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=102, random_state=100,
subsample=0.65))])
cv score: [0.625 0.64511494 0.48706897 0.59302326 0.46124031]
----------------------------------------
Trial 2464
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=132, random_state=100))])
cv score: [0.55172414 0.67600575 0.61637931 0.60723514 0.39211886]
----------------------------------------
Trial 2465
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=117,
random_state=100))])
cv score: [0.60344828 0.68534483 0.61350575 0.71705426 0.43217054]
----------------------------------------
Trial 2466
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, n_estimators=154,
random_state=100,
subsample=0.75))])
cv score: [0.61494253 0.68390805 0.62068966 0.64082687 0.45736434]
----------------------------------------
Trial 2467
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
n_estimators=130, random_state=100,
subsample=0.6))])
cv score: [0.55316092 0.70833333 0.54022989 0.4496124 0.47416021]
----------------------------------------
Trial 2468
----------------------------------------
Parameters {'xgb__n_estimators': 63, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=63,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51867816 0.69827586 0.58908046 0.61627907 0.44315245]
----------------------------------------
Trial 2469
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=58,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.625 0.47557471 0.52842377 0.46899225]
----------------------------------------
Trial 2470
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='log2',
n_estimators=67,
random_state=100))])
cv score: [0.66235632 0.70114943 0.65948276 0.66795866 0.43281654]
----------------------------------------
Trial 2471
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.65804598 0.65373563 0.65633075 0.42635659]
----------------------------------------
Trial 2472
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=197,
random_state=100))])
cv score: [0.61278736 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 2473
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=154,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 2474
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.50287356 0.69396552 0.60201149 0.53359173 0.4625323 ]
----------------------------------------
Trial 2475
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=130, random_state=100,
subsample=0.75))])
cv score: [0.65517241 0.63218391 0.48706897 0.65374677 0.45607235]
----------------------------------------
Trial 2476
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=199,
random_state=100))])
cv score: [0.51364943 0.63362069 0.53017241 0.58268734 0.29909561]
----------------------------------------
Trial 2477
----------------------------------------
Parameters {'rf__n_estimators': 25, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=25, random_state=100))])
cv score: [0.65158046 0.68175287 0.47413793 0.67377261 0.44573643]
----------------------------------------
Trial 2478
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.68247126 0.61637931 0.52971576 0.43410853]
----------------------------------------
Trial 2479
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.73850575 0.59770115 0.65245478 0.41860465]
----------------------------------------
Trial 2480
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=136,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74497126 0.65086207 0.72270115 0.62726098 0.40697674]
----------------------------------------
Trial 2481
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.75143678 0.59913793 0.64082687 0.4496124 ]
----------------------------------------
Trial 2482
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=83,
random_state=100))])
cv score: [0.6091954 0.68821839 0.60344828 0.71059432 0.37080103]
----------------------------------------
Trial 2483
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
n_estimators=159, random_state=100,
subsample=0.9))])
cv score: [0.5933908 0.74568966 0.56896552 0.51937984 0.4250646 ]
----------------------------------------
Trial 2484
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=88, random_state=100))])
cv score: [0.59051724 0.68318966 0.64798851 0.67958656 0.44509044]
----------------------------------------
Trial 2485
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=124,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.70402299 0.58045977 0.5878553 0.42764858]
----------------------------------------
Trial 2486
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=45, random_state=100))])
cv score: [0.51436782 0.65373563 0.57902299 0.55361757 0.44702842]
----------------------------------------
Trial 2487
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77586207 0.66522989 0.67241379 0.625323 0.4005168 ]
----------------------------------------
Trial 2488
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=128,
random_state=100))])
cv score: [0.55316092 0.65804598 0.58908046 0.74031008 0.42700258]
----------------------------------------
Trial 2489
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69683908 0.70689655 0.54885057 0.63178295 0.42894057]
----------------------------------------
Trial 2490
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.67241379 0.64655172 0.58010336 0.47932817]
----------------------------------------
Trial 2491
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=28, random_state=100))])
cv score: [0.54094828 0.65229885 0.50287356 0.63307494 0.5497416 ]
----------------------------------------
Trial 2492
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75431034 0.68534483 0.68534483 0.624677 0.43281654]
----------------------------------------
Trial 2493
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.60344828 0.64655172 0.63307494 0.38888889]
----------------------------------------
Trial 2494
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='log2',
n_estimators=116, random_state=100,
subsample=0.95))])
cv score: [0.55747126 0.72126437 0.59770115 0.65116279 0.43540052]
----------------------------------------
Trial 2495
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.71551724 0.57758621 0.63049096 0.40697674]
----------------------------------------
Trial 2496
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=132,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 2497
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=132, random_state=100))])
cv score: [0.63218391 0.63936782 0.62643678 0.59431525 0.40439276]
----------------------------------------
Trial 2498
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70689655 0.61494253 0.68821839 0.57364341 0.49289406]
----------------------------------------
Trial 2499
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=61, random_state=100))])
cv score: [0.55316092 0.6170977 0.59195402 0.53875969 0.42700258]
----------------------------------------
Trial 2500
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=75, random_state=100,
subsample=0.75))])
cv score: [0.44755747 0.59482759 0.50574713 0.61498708 0.57881137]
----------------------------------------
Trial 2501
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.64942529 0.65229885 0.65633075 0.39018088]
----------------------------------------
Trial 2502
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=164,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79166667 0.56393678 0.60416667 0.52713178 0.39018088]
----------------------------------------
Trial 2503
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=183,
random_state=100))])
cv score: [0.60488506 0.68390805 0.56321839 0.68475452 0.42764858]
----------------------------------------
Trial 2504
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=61, random_state=100))])
cv score: [0.57686782 0.71408046 0.64727011 0.69379845 0.44573643]
----------------------------------------
Trial 2505
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=52, random_state=100))])
cv score: [0.70977011 0.66954023 0.64655172 0.62015504 0.34625323]
----------------------------------------
Trial 2506
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=162, random_state=100,
subsample=0.75))])
cv score: [0.6566092 0.66666667 0.60632184 0.62144703 0.42764858]
----------------------------------------
Trial 2507
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=57,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.63649425 0.75431034 0.59689922 0.37338501]
----------------------------------------
Trial 2508
----------------------------------------
Parameters {'xgb__n_estimators': 26, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=26,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77658046 0.58548851 0.64942529 0.59625323 0.42571059]
----------------------------------------
Trial 2509
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.6))])
cv score: [0.65948276 0.61494253 0.69683908 0.57881137 0.42377261]
----------------------------------------
Trial 2510
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78376437 0.59051724 0.60847701 0.5497416 0.39147287]
----------------------------------------
Trial 2511
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=91,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64511494 0.67385057 0.66091954 0.56718346 0.40568475]
----------------------------------------
Trial 2512
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=160, random_state=100))])
cv score: [0.7183908 0.63649425 0.66235632 0.58914729 0.40826873]
----------------------------------------
Trial 2513
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=162, random_state=100))])
cv score: [0.63649425 0.63649425 0.61494253 0.61111111 0.39922481]
----------------------------------------
Trial 2514
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=194, random_state=100))])
cv score: [0.59770115 0.72988506 0.63793103 0.67700258 0.43669251]
----------------------------------------
Trial 2515
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=100,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.63218391 0.62356322 0.58139535 0.44315245]
----------------------------------------
Trial 2516
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=183,
random_state=100))])
cv score: [0.54166667 0.65373563 0.59195402 0.74289406 0.40245478]
----------------------------------------
Trial 2517
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='log2',
n_estimators=143, random_state=100,
subsample=0.95))])
cv score: [0.64224138 0.6408046 0.57183908 0.64470284 0.44186047]
----------------------------------------
Trial 2518
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65948276 0.63218391 0.67385057 0.60852713 0.40439276]
----------------------------------------
Trial 2519
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='log2',
n_estimators=138, random_state=100,
subsample=0.65))])
cv score: [0.54597701 0.6566092 0.4454023 0.5994832 0.44056848]
----------------------------------------
Trial 2520
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='sqrt',
n_estimators=123, random_state=100,
subsample=0.75))])
cv score: [0.68247126 0.69109195 0.5862069 0.625323 0.42764858]
----------------------------------------
Trial 2521
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=101,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76939655 0.58979885 0.69252874 0.60788114 0.4379845 ]
----------------------------------------
Trial 2522
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='log2',
n_estimators=51, random_state=100,
subsample=0.65))])
cv score: [0.63074713 0.69396552 0.54597701 0.7118863 0.41989664]
----------------------------------------
Trial 2523
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.67816092 0.65804598 0.61757106 0.4496124 ]
----------------------------------------
Trial 2524
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=12,
random_state=100))])
cv score: [0.6408046 0.59770115 0.61135057 0.6001292 0.41343669]
----------------------------------------
Trial 2525
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=51,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52011494 0.70689655 0.72557471 0.624677 0.37984496]
----------------------------------------
Trial 2526
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=47, random_state=100,
subsample=0.75))])
cv score: [0.5704023 0.59770115 0.49856322 0.65116279 0.45994832]
----------------------------------------
Trial 2527
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77801724 0.60704023 0.68965517 0.62403101 0.43475452]
----------------------------------------
Trial 2528
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=31, random_state=100,
subsample=0.9))])
cv score: [0.74425287 0.6566092 0.6637931 0.66731266 0.4379845 ]
----------------------------------------
Trial 2529
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=190, random_state=100,
subsample=0.8))])
cv score: [0.73132184 0.64224138 0.63936782 0.63307494 0.43410853]
----------------------------------------
Trial 2530
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77801724 0.58189655 0.67385057 0.63113695 0.38049096]
----------------------------------------
Trial 2531
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.65229885 0.67672414 0.63953488 0.41472868]
----------------------------------------
Trial 2532
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=53, random_state=100,
subsample=0.75))])
cv score: [0.59195402 0.72270115 0.55028736 0.66795866 0.48191214]
----------------------------------------
Trial 2533
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=163,
random_state=100))])
cv score: [0.54454023 0.66091954 0.5862069 0.74289406 0.41085271]
----------------------------------------
Trial 2534
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76005747 0.5783046 0.62356322 0.624677 0.45090439]
----------------------------------------
Trial 2535
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='log2',
n_estimators=169, random_state=100,
subsample=0.65))])
cv score: [0.67097701 0.66091954 0.56321839 0.68217054 0.46770026]
----------------------------------------
Trial 2536
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=167,
random_state=100))])
cv score: [0.59626437 0.67528736 0.62068966 0.68087855 0.41989664]
----------------------------------------
Trial 2537
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77729885 0.61206897 0.66666667 0.624677 0.4496124 ]
----------------------------------------
Trial 2538
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=35, random_state=100))])
cv score: [0.5 0.67025862 0.5941092 0.54844961 0.45671835]
----------------------------------------
Trial 2539
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=194,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 2540
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=71, random_state=100))])
cv score: [0.75718391 0.62571839 0.53663793 0.58527132 0.38501292]
----------------------------------------
Trial 2541
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=10, random_state=100,
subsample=0.7))])
cv score: [0.65804598 0.7112069 0.38936782 0.67183463 0.53682171]
----------------------------------------
Trial 2542
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=155,
random_state=100))])
cv score: [0.77729885 0.63793103 0.68247126 0.6369509 0.39793282]
----------------------------------------
Trial 2543
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=143,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73778736 0.5941092 0.72413793 0.5626615 0.38113695]
----------------------------------------
Trial 2544
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.75143678 0.56752874 0.59689922 0.41989664]
----------------------------------------
Trial 2545
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=90, random_state=100,
subsample=0.85))])
cv score: [0.66954023 0.60488506 0.52586207 0.68604651 0.48320413]
----------------------------------------
Trial 2546
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=136, random_state=100))])
cv score: [0.65229885 0.67241379 0.61350575 0.59560724 0.43669251]
----------------------------------------
Trial 2547
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=178, random_state=100))])
cv score: [0.56752874 0.64942529 0.61278736 0.63307494 0.40374677]
----------------------------------------
Trial 2548
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=24,
random_state=100))])
cv score: [0.62643678 0.65086207 0.55172414 0.57881137 0.46124031]
----------------------------------------
Trial 2549
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.65517241 0.61350575 0.57622739 0.42764858]
----------------------------------------
Trial 2550
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=23, random_state=100,
subsample=0.85))])
cv score: [0.69827586 0.56609195 0.63936782 0.64728682 0.31007752]
----------------------------------------
Trial 2551
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='log2',
n_estimators=110,
random_state=100))])
cv score: [0.67528736 0.62931034 0.47844828 0.52713178 0.54134367]
----------------------------------------
Trial 2552
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=74, random_state=100,
subsample=0.85))])
cv score: [0.65373563 0.57327586 0.59913793 0.62919897 0.41602067]
----------------------------------------
Trial 2553
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.9))])
cv score: [0.6954023 0.62356322 0.5 0.66925065 0.58010336]
----------------------------------------
Trial 2554
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=12,
random_state=100))])
cv score: [0.61997126 0.60057471 0.5545977 0.57105943 0.45348837]
----------------------------------------
Trial 2555
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=55,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.81537356 0.63002874 0.69899425 0.65374677 0.44509044]
----------------------------------------
Trial 2556
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=89,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79741379 0.57686782 0.62787356 0.62144703 0.4625323 ]
----------------------------------------
Trial 2557
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=73, random_state=100,
subsample=0.65))])
cv score: [0.5545977 0.71982759 0.59195402 0.68346253 0.43281654]
----------------------------------------
Trial 2558
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74425287 0.63793103 0.68678161 0.61175711 0.43604651]
----------------------------------------
Trial 2559
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=132,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.73132184 0.65373563 0.58268734 0.46770026]
----------------------------------------
Trial 2560
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=62,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.65804598 0.51149425 0.63307494 0.44186047]
----------------------------------------
Trial 2561
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=42,
random_state=100))])
cv score: [0.57255747 0.60272989 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 2562
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=123, random_state=100))])
cv score: [0.60488506 0.70761494 0.64942529 0.6744186 0.42312661]
----------------------------------------
Trial 2563
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=50,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52586207 0.60344828 0.60632184 0.53875969 0.45865633]
----------------------------------------
Trial 2564
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
n_estimators=126, random_state=100,
subsample=0.85))])
cv score: [0.5545977 0.71408046 0.60488506 0.71447028 0.40956072]
----------------------------------------
Trial 2565
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=189, random_state=100))])
cv score: [0.59913793 0.74425287 0.65014368 0.68410853 0.43540052]
----------------------------------------
Trial 2566
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6795977 0.6566092 0.69683908 0.58268734 0.44702842]
----------------------------------------
Trial 2567
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=160, random_state=100))])
cv score: [0.63074713 0.65373563 0.58333333 0.67700258 0.39147287]
----------------------------------------
Trial 2568
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=194,
random_state=100))])
cv score: [0.57758621 0.61206897 0.49425287 0.57235142 0.49095607]
----------------------------------------
Trial 2569
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=150,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80890805 0.60991379 0.64655172 0.62403101 0.46899225]
----------------------------------------
Trial 2570
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=84, random_state=100,
subsample=0.95))])
cv score: [0.59770115 0.63793103 0.53591954 0.59689922 0.45607235]
----------------------------------------
Trial 2571
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=82,
random_state=100))])
cv score: [0.80172414 0.61781609 0.66594828 0.62855297 0.3875969 ]
----------------------------------------
Trial 2572
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.67528736 0.60488506 0.60077519 0.40310078]
----------------------------------------
Trial 2573
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=104,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.69683908 0.64798851 0.62403101 0.4005168 ]
----------------------------------------
Trial 2574
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=15, random_state=100,
subsample=0.75))])
cv score: [0.6954023 0.70689655 0.87068966 0.63565891 0.45219638]
----------------------------------------
Trial 2575
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=184,
random_state=100))])
cv score: [0.56321839 0.61063218 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 2576
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=118,
random_state=100))])
cv score: [0.55603448 0.66020115 0.60201149 0.74031008 0.41795866]
----------------------------------------
Trial 2577
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78520115 0.58692529 0.6566092 0.61950904 0.44121447]
----------------------------------------
Trial 2578
----------------------------------------
Parameters {'xgb__n_estimators': 99, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=99,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61781609 0.72126437 0.5545977 0.59302326 0.42894057]
----------------------------------------
Trial 2579
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71982759 0.56824713 0.66882184 0.6369509 0.40116279]
----------------------------------------
Trial 2580
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=54, random_state=100))])
cv score: [0.71551724 0.66810345 0.60632184 0.62273902 0.4005168 ]
----------------------------------------
Trial 2581
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=37, random_state=100,
subsample=0.85))])
cv score: [0.58333333 0.64224138 0.56321839 0.57105943 0.31524548]
----------------------------------------
Trial 2582
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.68103448 0.55028736 0.61111111 0.40310078]
----------------------------------------
Trial 2583
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77873563 0.6566092 0.69252874 0.60335917 0.45671835]
----------------------------------------
Trial 2584
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56321839 0.68247126 0.58764368 0.55555556 0.43927649]
----------------------------------------
Trial 2585
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=75,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70977011 0.70545977 0.68103448 0.60335917 0.41020672]
----------------------------------------
Trial 2586
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=19,
random_state=100))])
cv score: [0.7887931 0.66810345 0.70833333 0.69056848 0.46770026]
----------------------------------------
Trial 2587
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77442529 0.6408046 0.68606322 0.65374677 0.38372093]
----------------------------------------
Trial 2588
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=112, random_state=100,
subsample=0.6))])
cv score: [0.63649425 0.74425287 0.63793103 0.68863049 0.5 ]
----------------------------------------
Trial 2589
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=59,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.61494253 0.54166667 0.5620155 0.5 ]
----------------------------------------
Trial 2590
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=98,
random_state=100))])
cv score: [0.63362069 0.66522989 0.59195402 0.62919897 0.4005168 ]
----------------------------------------
Trial 2591
----------------------------------------
Parameters {'xgb__n_estimators': 148, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=148,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.70402299 0.60488506 0.62273902 0.41472868]
----------------------------------------
Trial 2592
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57614943 0.67672414 0.5862069 0.60852713 0.43023256]
----------------------------------------
Trial 2593
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=137, random_state=100))])
cv score: [0.59841954 0.72413793 0.64727011 0.68410853 0.41925065]
----------------------------------------
Trial 2594
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.72270115 0.63793103 0.61627907 0.43023256]
----------------------------------------
Trial 2595
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=165, random_state=100,
subsample=0.65))])
cv score: [0.44109195 0.5 0.50574713 0.5620155 0.48837209]
----------------------------------------
Trial 2596
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=146,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56465517 0.68390805 0.55890805 0.63307494 0.43540052]
----------------------------------------
Trial 2597
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='sqrt',
n_estimators=71, random_state=100,
subsample=0.6))])
cv score: [0.58908046 0.60201149 0.50431034 0.60206718 0.37855297]
----------------------------------------
Trial 2598
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=104,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.69396552 0.63074713 0.60206718 0.39018088]
----------------------------------------
Trial 2599
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=169,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79956897 0.60632184 0.70689655 0.63824289 0.44379845]
----------------------------------------
Trial 2600
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=42,
random_state=100))])
cv score: [0.61063218 0.64367816 0.625 0.66989664 0.39018088]
----------------------------------------
Trial 2601
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=56,
random_state=100))])
cv score: [0.6566092 0.63793103 0.64942529 0.61498708 0.40439276]
----------------------------------------
Trial 2602
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.6))])
cv score: [0.61925287 0.6637931 0.56321839 0.65116279 0.41602067]
----------------------------------------
Trial 2603
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=31, random_state=100))])
cv score: [0.6795977 0.6716954 0.49856322 0.64082687 0.45930233]
----------------------------------------
Trial 2604
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=84, random_state=100,
subsample=0.65))])
cv score: [0.55890805 0.6566092 0.59913793 0.54651163 0.40439276]
----------------------------------------
Trial 2605
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=12, random_state=100,
subsample=0.95))])
cv score: [0.62068966 0.65804598 0.63362069 0.58397933 0.3630491 ]
----------------------------------------
Trial 2606
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.72701149 0.53591954 0.57622739 0.42764858]
----------------------------------------
Trial 2607
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='log2',
n_estimators=132, random_state=100,
subsample=0.7))])
cv score: [0.67816092 0.67672414 0.55603448 0.66408269 0.48191214]
----------------------------------------
Trial 2608
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=195,
random_state=100))])
cv score: [0.7658046 0.63649425 0.69396552 0.63953488 0.39147287]
----------------------------------------
Trial 2609
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='log2',
n_estimators=178, random_state=100,
subsample=0.6))])
cv score: [0.62068966 0.67672414 0.56609195 0.61886305 0.39922481]
----------------------------------------
Trial 2610
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=116, random_state=100))])
cv score: [0.60775862 0.6795977 0.59195402 0.62919897 0.41989664]
----------------------------------------
Trial 2611
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.65517241 0.65229885 0.59819121 0.41343669]
----------------------------------------
Trial 2612
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=144,
random_state=100))])
cv score: [0.75143678 0.65373563 0.69109195 0.64470284 0.43669251]
----------------------------------------
Trial 2613
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=192,
random_state=100))])
cv score: [0.62068966 0.6566092 0.60344828 0.67958656 0.45090439]
----------------------------------------
Trial 2614
----------------------------------------
Parameters {'xgb__n_estimators': 171, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=171,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57758621 0.63074713 0.76724138 0.55555556 0.45736434]
----------------------------------------
Trial 2615
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
n_estimators=122,
random_state=100))])
cv score: [0.50574713 0.61637931 0.53376437 0.63049096 0.45090439]
----------------------------------------
Trial 2616
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=141,
random_state=100))])
cv score: [0.59195402 0.67887931 0.64583333 0.71705426 0.40697674]
----------------------------------------
Trial 2617
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=40, random_state=100))])
cv score: [0.55747126 0.6954023 0.62859195 0.59496124 0.46059432]
----------------------------------------
Trial 2618
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=110, random_state=100,
subsample=0.9))])
cv score: [0.64367816 0.70833333 0.50862069 0.55426357 0.45090439]
----------------------------------------
Trial 2619
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=186,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.72557471 0.55316092 0.55943152 0.42635659]
----------------------------------------
Trial 2620
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=76,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7808908 0.58117816 0.67887931 0.62984496 0.38049096]
----------------------------------------
Trial 2621
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=183, random_state=100))])
cv score: [0.61350575 0.6637931 0.5862069 0.62144703 0.44444444]
----------------------------------------
Trial 2622
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=176,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56752874 0.70833333 0.61494253 0.58527132 0.38113695]
----------------------------------------
Trial 2623
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=11, random_state=100))])
cv score: [0.67887931 0.59913793 0.4533046 0.62273902 0.37338501]
----------------------------------------
Trial 2624
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.72413793 0.61781609 0.63953488 0.40568475]
----------------------------------------
Trial 2625
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=152, random_state=100))])
cv score: [0.63793103 0.63936782 0.62356322 0.59173127 0.39664083]
----------------------------------------
Trial 2626
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=155, random_state=100,
subsample=0.75))])
cv score: [0.52011494 0.72701149 0.60775862 0.69767442 0.4121447 ]
----------------------------------------
Trial 2627
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=155, random_state=100))])
cv score: [0.72844828 0.64655172 0.6637931 0.66408269 0.42764858]
----------------------------------------
Trial 2628
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=43,
random_state=100))])
cv score: [0.68965517 0.63362069 0.67816092 0.68087855 0.37726098]
----------------------------------------
Trial 2629
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=197,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.73132184 0.59626437 0.61627907 0.41343669]
----------------------------------------
Trial 2630
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=197, random_state=100))])
cv score: [0.67097701 0.63362069 0.59482759 0.61111111 0.40180879]
----------------------------------------
Trial 2631
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=141,
random_state=100))])
cv score: [0.59195402 0.67887931 0.64583333 0.71705426 0.40697674]
----------------------------------------
Trial 2632
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73778736 0.56537356 0.71551724 0.56395349 0.38113695]
----------------------------------------
Trial 2633
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=33,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7420977 0.6012931 0.7341954 0.54651163 0.44896641]
----------------------------------------
Trial 2634
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=25, random_state=100,
subsample=0.8))])
cv score: [0.5387931 0.51005747 0.52873563 0.67700258 0.49354005]
----------------------------------------
Trial 2635
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=155,
random_state=100))])
cv score: [0.58764368 0.68606322 0.61637931 0.70671835 0.39922481]
----------------------------------------
Trial 2636
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=17,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.66594828 0.69181034 0.62984496 0.43927649]
----------------------------------------
Trial 2637
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=47, random_state=100))])
cv score: [0.73850575 0.62571839 0.61781609 0.62015504 0.39082687]
----------------------------------------
Trial 2638
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=136,
random_state=100))])
cv score: [0.61278736 0.59913793 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 2639
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=195,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.66091954 0.64942529 0.62790698 0.40439276]
----------------------------------------
Trial 2640
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='sqrt',
n_estimators=120, random_state=100,
subsample=0.85))])
cv score: [0.72126437 0.69396552 0.59626437 0.61369509 0.50516796]
----------------------------------------
Trial 2641
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.65517241 0.56609195 0.66537468 0.40310078]
----------------------------------------
Trial 2642
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=33,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.7183908 0.52155172 0.66666667 0.45607235]
----------------------------------------
Trial 2643
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=43, random_state=100))])
cv score: [0.61494253 0.625 0.70689655 0.57235142 0.42118863]
----------------------------------------
Trial 2644
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=159, random_state=100,
subsample=0.7))])
cv score: [0.7112069 0.6637931 0.44396552 0.62661499 0.46511628]
----------------------------------------
Trial 2645
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=103,
random_state=100))])
cv score: [0.61278736 0.67887931 0.63721264 0.70671835 0.38436693]
----------------------------------------
Trial 2646
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=58,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.62068966 0.72988506 0.61175711 0.41472868]
----------------------------------------
Trial 2647
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.68390805 0.5158046 0.59302326 0.45736434]
----------------------------------------
Trial 2648
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=39, random_state=100,
subsample=0.95))])
cv score: [0.6954023 0.62787356 0.57614943 0.55167959 0.48191214]
----------------------------------------
Trial 2649
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=20,
random_state=100))])
cv score: [0.5933908 0.55387931 0.47198276 0.60335917 0.43992248]
----------------------------------------
Trial 2650
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=145, random_state=100))])
cv score: [0.61350575 0.69971264 0.66810345 0.67571059 0.42571059]
----------------------------------------
Trial 2651
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=160, random_state=100,
subsample=0.95))])
cv score: [0.59770115 0.70977011 0.50143678 0.63953488 0.45348837]
----------------------------------------
Trial 2652
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=117, random_state=100,
subsample=0.9))])
cv score: [0.57758621 0.69252874 0.45833333 0.61627907 0.4625323 ]
----------------------------------------
Trial 2653
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=123, random_state=100))])
cv score: [0.66954023 0.68534483 0.59482759 0.63436693 0.42118863]
----------------------------------------
Trial 2654
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='sqrt',
n_estimators=119, random_state=100,
subsample=0.6))])
cv score: [0.68678161 0.66235632 0.61494253 0.69638243 0.45607235]
----------------------------------------
Trial 2655
----------------------------------------
Parameters {'gb__n_estimators': 96, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=96, random_state=100,
subsample=0.75))])
cv score: [0.68678161 0.69683908 0.50574713 0.60335917 0.52067183]
----------------------------------------
Trial 2656
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=22,
random_state=100))])
cv score: [0.6170977 0.60488506 0.55675287 0.51744186 0.46963824]
----------------------------------------
Trial 2657
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=23,
random_state=100))])
cv score: [0.58189655 0.68247126 0.62931034 0.53617571 0.48966408]
----------------------------------------
Trial 2658
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.70545977 0.61494253 0.56459948 0.4121447 ]
----------------------------------------
Trial 2659
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=101,
random_state=100))])
cv score: [0.68031609 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 2660
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=116, random_state=100,
subsample=0.8))])
cv score: [0.59770115 0.75 0.58764368 0.69638243 0.43023256]
----------------------------------------
Trial 2661
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80747126 0.57758621 0.65158046 0.66472868 0.38953488]
----------------------------------------
Trial 2662
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='log2',
n_estimators=114,
random_state=100))])
cv score: [0.63793103 0.63936782 0.47701149 0.58914729 0.46770026]
----------------------------------------
Trial 2663
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=38,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 2664
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.69683908 0.68103448 0.55943152 0.45284238]
----------------------------------------
Trial 2665
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.56681034 0.47126437 0.53165375 0.41860465]
----------------------------------------
Trial 2666
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7183908 0.68678161 0.73275862 0.63113695 0.37790698]
----------------------------------------
Trial 2667
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=30,
random_state=100))])
cv score: [0.60560345 0.62643678 0.64367816 0.64018088 0.39664083]
----------------------------------------
Trial 2668
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=85,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.65229885 0.6566092 0.58914729 0.41085271]
----------------------------------------
Trial 2669
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=73, random_state=100,
subsample=0.85))])
cv score: [0.53591954 0.68821839 0.55603448 0.60723514 0.45607235]
----------------------------------------
Trial 2670
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=66,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.60201149 0.58045977 0.49870801 0.45736434]
----------------------------------------
Trial 2671
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=140, random_state=100))])
cv score: [0.66810345 0.68821839 0.60632184 0.65762274 0.41860465]
----------------------------------------
Trial 2672
----------------------------------------
Parameters {'gb__n_estimators': 177, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=177, random_state=100,
subsample=0.85))])
cv score: [0.54597701 0.71551724 0.57471264 0.68087855 0.40310078]
----------------------------------------
Trial 2673
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.58477011 0.47126437 0.53229974 0.39082687]
----------------------------------------
Trial 2674
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=49,
random_state=100))])
cv score: [0.61206897 0.63218391 0.61781609 0.70284238 0.374677 ]
----------------------------------------
Trial 2675
----------------------------------------
Parameters {'xgb__n_estimators': 26, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=26,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63793103 0.6091954 0.48275862 0.5749354 0.43023256]
----------------------------------------
Trial 2676
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=55, random_state=100))])
cv score: [0.71264368 0.66666667 0.60632184 0.62144703 0.40439276]
----------------------------------------
Trial 2677
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=198, random_state=100))])
cv score: [0.67241379 0.63362069 0.59626437 0.6124031 0.40826873]
----------------------------------------
Trial 2678
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=40,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52729885 0.69468391 0.73132184 0.66795866 0.3869509 ]
----------------------------------------
Trial 2679
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=112, random_state=100,
subsample=0.95))])
cv score: [0.53017241 0.70114943 0.61063218 0.63307494 0.38436693]
----------------------------------------
Trial 2680
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=115, random_state=100))])
cv score: [0.61063218 0.70905172 0.64942529 0.68217054 0.42248062]
----------------------------------------
Trial 2681
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=85, random_state=100,
subsample=0.65))])
cv score: [0.74856322 0.5933908 0.70833333 0.58268734 0.40374677]
----------------------------------------
Trial 2682
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=145, random_state=100,
subsample=0.95))])
cv score: [0.72844828 0.64942529 0.70258621 0.66020672 0.38630491]
----------------------------------------
Trial 2683
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, n_estimators=108,
random_state=100,
subsample=0.85))])
cv score: [0.57183908 0.73706897 0.60488506 0.66408269 0.40439276]
----------------------------------------
Trial 2684
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=24,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63362069 0.74137931 0.75574713 0.63501292 0.42958656]
----------------------------------------
Trial 2685
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=33, random_state=100))])
cv score: [0.59985632 0.68247126 0.55100575 0.67377261 0.42700258]
----------------------------------------
Trial 2686
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=186,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56752874 0.70833333 0.59626437 0.60465116 0.43281654]
----------------------------------------
Trial 2687
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='log2',
n_estimators=61, random_state=100,
subsample=0.6))])
cv score: [0.67241379 0.66522989 0.37212644 0.53617571 0.51033592]
----------------------------------------
Trial 2688
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=94, random_state=100,
subsample=0.95))])
cv score: [0.60057471 0.73563218 0.61781609 0.6498708 0.38372093]
----------------------------------------
Trial 2689
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=142, random_state=100,
subsample=0.6))])
cv score: [0.68965517 0.66522989 0.59482759 0.6627907 0.48449612]
----------------------------------------
Trial 2690
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=186, random_state=100))])
cv score: [0.59841954 0.73994253 0.65229885 0.6750646 0.44832041]
----------------------------------------
Trial 2691
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=191, random_state=100))])
cv score: [0.59482759 0.6637931 0.57471264 0.64082687 0.43023256]
----------------------------------------
Trial 2692
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=76,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67600575 0.70114943 0.70617816 0.66343669 0.34883721]
----------------------------------------
Trial 2693
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=137, random_state=100))])
cv score: [0.65229885 0.68103448 0.62212644 0.59560724 0.4379845 ]
----------------------------------------
Trial 2694
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=108,
random_state=100))])
cv score: [0.78017241 0.63218391 0.67097701 0.63307494 0.39793282]
----------------------------------------
Trial 2695
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68821839 0.66522989 0.69396552 0.65503876 0.42571059]
----------------------------------------
Trial 2696
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=65,
random_state=100))])
cv score: [0.68821839 0.62931034 0.67097701 0.65374677 0.38113695]
----------------------------------------
Trial 2697
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
n_estimators=106, random_state=100,
subsample=0.85))])
cv score: [0.52586207 0.66810345 0.54310345 0.6124031 0.36821705]
----------------------------------------
Trial 2698
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=32, random_state=100,
subsample=0.8))])
cv score: [0.62212644 0.62356322 0.43103448 0.65891473 0.49612403]
----------------------------------------
Trial 2699
----------------------------------------
Parameters {'gb__n_estimators': 133, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=133, random_state=100,
subsample=0.8))])
cv score: [0.58908046 0.66810345 0.57902299 0.63824289 0.43927649]
----------------------------------------
Trial 2700
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=101,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71695402 0.69683908 0.7183908 0.60658915 0.43281654]
----------------------------------------
Trial 2701
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=84, random_state=100))])
cv score: [0.5862069 0.68606322 0.64224138 0.67183463 0.44121447]
----------------------------------------
Trial 2702
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=192, random_state=100,
subsample=0.85))])
cv score: [0.59195402 0.64942529 0.5 0.62273902 0.4625323 ]
----------------------------------------
Trial 2703
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=45, random_state=100,
subsample=0.65))])
cv score: [0.54885057 0.68390805 0.57614943 0.68992248 0.4005168 ]
----------------------------------------
Trial 2704
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=94,
random_state=100))])
cv score: [0.61925287 0.64798851 0.58764368 0.66020672 0.41602067]
----------------------------------------
Trial 2705
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='log2',
n_estimators=189, random_state=100,
subsample=0.95))])
cv score: [0.58908046 0.68103448 0.41522989 0.59689922 0.52325581]
----------------------------------------
Trial 2706
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=18, random_state=100,
subsample=0.75))])
cv score: [0.77155172 0.66738506 0.58908046 0.64664083 0.43669251]
----------------------------------------
Trial 2707
----------------------------------------
Parameters {'rf__n_estimators': 92, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=92, random_state=100))])
cv score: [0.57471264 0.71336207 0.64152299 0.64857881 0.44121447]
----------------------------------------
Trial 2708
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
n_estimators=128, random_state=100,
subsample=0.9))])
cv score: [0.5933908 0.61925287 0.55316092 0.74289406 0.45736434]
----------------------------------------
Trial 2709
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=89, random_state=100))])
cv score: [0.73275862 0.6091954 0.55747126 0.56847545 0.43023256]
----------------------------------------
Trial 2710
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='log2',
n_estimators=155, random_state=100,
subsample=0.75))])
cv score: [0.58045977 0.63218391 0.54741379 0.64857881 0.43281654]
----------------------------------------
Trial 2711
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=178,
random_state=100))])
cv score: [0.61278736 0.60201149 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 2712
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=191,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.71695402 0.68678161 0.63565891 0.38501292]
----------------------------------------
Trial 2713
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=177,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55172414 0.63505747 0.51149425 0.58656331 0.50645995]
----------------------------------------
Trial 2714
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=181, random_state=100,
subsample=0.95))])
cv score: [0.63649425 0.65948276 0.52586207 0.60594315 0.45219638]
----------------------------------------
Trial 2715
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='sqrt',
n_estimators=85, random_state=100,
subsample=0.75))])
cv score: [0.59482759 0.4683908 0.55818966 0.32428941 0.45736434]
----------------------------------------
Trial 2716
----------------------------------------
Parameters {'rf__n_estimators': 62, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=62, random_state=100))])
cv score: [0.72413793 0.65086207 0.67385057 0.59819121 0.37726098]
----------------------------------------
Trial 2717
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=134, random_state=100))])
cv score: [0.60775862 0.7191092 0.64367816 0.68475452 0.41925065]
----------------------------------------
Trial 2718
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=156, random_state=100,
subsample=0.75))])
cv score: [0.68534483 0.67672414 0.58045977 0.64728682 0.44186047]
----------------------------------------
Trial 2719
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='sqrt',
n_estimators=103, random_state=100,
subsample=0.8))])
cv score: [0.54166667 0.64224138 0.49137931 0.54263566 0.47803618]
----------------------------------------
Trial 2720
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=35, random_state=100))])
cv score: [0.55316092 0.65517241 0.44324713 0.65956072 0.44573643]
----------------------------------------
Trial 2721
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=71,
random_state=100))])
cv score: [0.78232759 0.60344828 0.67887931 0.62661499 0.4005168 ]
----------------------------------------
Trial 2722
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5387931 0.72413793 0.63505747 0.62273902 0.38113695]
----------------------------------------
Trial 2723
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58764368 0.71982759 0.60201149 0.62919897 0.43152455]
----------------------------------------
Trial 2724
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.81178161 0.58692529 0.68534483 0.6750646 0.46382429]
----------------------------------------
Trial 2725
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54741379 0.72844828 0.55747126 0.62015504 0.4379845 ]
----------------------------------------
Trial 2726
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=84, random_state=100,
subsample=0.9))])
cv score: [0.52586207 0.72701149 0.55316092 0.6124031 0.45865633]
----------------------------------------
Trial 2727
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=87, random_state=100))])
cv score: [0.63793103 0.64942529 0.50287356 0.6627907 0.36821705]
----------------------------------------
Trial 2728
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=132, random_state=100,
subsample=0.65))])
cv score: [0.63362069 0.68103448 0.60344828 0.67829457 0.4379845 ]
----------------------------------------
Trial 2729
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=71,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71982759 0.65229885 0.71695402 0.65826873 0.42700258]
----------------------------------------
Trial 2730
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7579023 0.55890805 0.70474138 0.64793282 0.46317829]
----------------------------------------
Trial 2731
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=152, random_state=100))])
cv score: [0.57327586 0.68031609 0.59770115 0.58656331 0.45284238]
----------------------------------------
Trial 2732
----------------------------------------
Parameters {'xgb__n_estimators': 145, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=145,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.72054598 0.75287356 0.64470284 0.37273902]
----------------------------------------
Trial 2733
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=20, random_state=100))])
cv score: [0.51652299 0.61422414 0.54525862 0.57428941 0.50839793]
----------------------------------------
Trial 2734
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=169,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.70402299 0.57327586 0.67958656 0.42248062]
----------------------------------------
Trial 2735
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67241379 0.63218391 0.67816092 0.58268734 0.42894057]
----------------------------------------
Trial 2736
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.58764368 0.47126437 0.54134367 0.33850129]
----------------------------------------
Trial 2737
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=151, random_state=100,
subsample=0.9))])
cv score: [0.53735632 0.64655172 0.54597701 0.71447028 0.43281654]
----------------------------------------
Trial 2738
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.70689655 0.58333333 0.64341085 0.43281654]
----------------------------------------
Trial 2739
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=163, random_state=100))])
cv score: [0.60201149 0.66810345 0.59626437 0.64082687 0.43540052]
----------------------------------------
Trial 2740
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=137, random_state=100,
subsample=0.75))])
cv score: [0.64655172 0.69252874 0.71408046 0.71963824 0.44444444]
----------------------------------------
Trial 2741
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=72, random_state=100,
subsample=0.6))])
cv score: [0.62643678 0.68821839 0.64798851 0.66537468 0.49741602]
----------------------------------------
Trial 2742
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=36, random_state=100))])
cv score: [0.60847701 0.66810345 0.59698276 0.69509044 0.44573643]
----------------------------------------
Trial 2743
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=41,
random_state=100))])
cv score: [0.64942529 0.62931034 0.67672414 0.60852713 0.4496124 ]
----------------------------------------
Trial 2744
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=48, random_state=100))])
cv score: [0.55747126 0.63074713 0.66522989 0.62661499 0.39922481]
----------------------------------------
Trial 2745
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=153, random_state=100,
subsample=0.75))])
cv score: [0.62356322 0.65517241 0.46551724 0.60981912 0.54909561]
----------------------------------------
Trial 2746
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=147, random_state=100,
subsample=0.9))])
cv score: [0.59770115 0.72988506 0.56896552 0.62144703 0.40180879]
----------------------------------------
Trial 2747
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.65804598 0.66954023 0.63178295 0.44056848]
----------------------------------------
Trial 2748
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=83,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78304598 0.59482759 0.66091954 0.55620155 0.41602067]
----------------------------------------
Trial 2749
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=59,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78520115 0.61853448 0.68821839 0.60465116 0.49612403]
----------------------------------------
Trial 2750
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='sqrt',
n_estimators=141, random_state=100,
subsample=0.65))])
cv score: [0.6954023 0.68247126 0.63218391 0.5878553 0.54005168]
----------------------------------------
Trial 2751
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=83, random_state=100,
subsample=0.7))])
cv score: [0.67241379 0.375 0.61206897 0.54909561 0.4625323 ]
----------------------------------------
Trial 2752
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=49, random_state=100,
subsample=0.75))])
cv score: [0.60057471 0.65086207 0.53735632 0.64728682 0.50904393]
----------------------------------------
Trial 2753
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=63, random_state=100,
subsample=0.6))])
cv score: [0.54310345 0.67816092 0.50431034 0.57105943 0.40439276]
----------------------------------------
Trial 2754
----------------------------------------
Parameters {'xgb__n_estimators': 37, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=37,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72557471 0.55244253 0.5545977 0.57751938 0.49289406]
----------------------------------------
Trial 2755
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=55,
random_state=100))])
cv score: [0.65229885 0.63793103 0.56178161 0.57235142 0.36757106]
----------------------------------------
Trial 2756
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=51, random_state=100))])
cv score: [0.7112069 0.68534483 0.63505747 0.63307494 0.48837209]
----------------------------------------
Trial 2757
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=101,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77658046 0.59698276 0.72413793 0.62596899 0.42700258]
----------------------------------------
Trial 2758
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=129,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 2759
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=101, random_state=100))])
cv score: [0.59913793 0.69899425 0.65086207 0.65503876 0.43281654]
----------------------------------------
Trial 2760
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69396552 0.65732759 0.66954023 0.58850129 0.40762274]
----------------------------------------
Trial 2761
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=151, random_state=100,
subsample=0.95))])
cv score: [0.63793103 0.69396552 0.53304598 0.60077519 0.49612403]
----------------------------------------
Trial 2762
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56896552 0.69109195 0.63362069 0.61627907 0.43281654]
----------------------------------------
Trial 2763
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.58333333 0.59051724 0.55555556 0.45736434]
----------------------------------------
Trial 2764
----------------------------------------
Parameters {'gb__n_estimators': 16, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=16, random_state=100,
subsample=0.75))])
cv score: [0.5545977 0.65229885 0.60057471 0.64082687 0.39857881]
----------------------------------------
Trial 2765
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=66,
random_state=100))])
cv score: [0.56321839 0.61206897 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 2766
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=199,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.70689655 0.67241379 0.56847545 0.42248062]
----------------------------------------
Trial 2767
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70402299 0.66522989 0.66091954 0.59819121 0.48062016]
----------------------------------------
Trial 2768
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='sqrt',
n_estimators=188, random_state=100,
subsample=0.65))])
cv score: [0.56321839 0.66235632 0.45833333 0.56847545 0.44832041]
----------------------------------------
Trial 2769
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=71,
random_state=100))])
cv score: [0.58692529 0.60272989 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 2770
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=115,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 2771
----------------------------------------
Parameters {'gb__n_estimators': 135, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='sqrt',
n_estimators=135, random_state=100,
subsample=0.75))])
cv score: [0.64798851 0.69396552 0.41666667 0.62144703 0.49870801]
----------------------------------------
Trial 2772
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=84, random_state=100,
subsample=0.75))])
cv score: [0.625 0.71982759 0.61637931 0.64599483 0.41731266]
----------------------------------------
Trial 2773
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=177,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.68247126 0.5316092 0.59689922 0.43281654]
----------------------------------------
Trial 2774
----------------------------------------
Parameters {'rf__n_estimators': 54, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=54, random_state=100))])
cv score: [0.6408046 0.68247126 0.59913793 0.68992248 0.43217054]
----------------------------------------
Trial 2775
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=132, random_state=100,
subsample=0.8))])
cv score: [0.72988506 0.66091954 0.62787356 0.65245478 0.44702842]
----------------------------------------
Trial 2776
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='log2',
n_estimators=132, random_state=100,
subsample=0.9))])
cv score: [0.64511494 0.68247126 0.57614943 0.6498708 0.44832041]
----------------------------------------
Trial 2777
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65229885 0.61637931 0.63362069 0.625323 0.42635659]
----------------------------------------
Trial 2778
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=126, random_state=100,
subsample=0.8))])
cv score: [0.2658046 0.61637931 0.5862069 0.70671835 0.45348837]
----------------------------------------
Trial 2779
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=98, random_state=100))])
cv score: [0.70402299 0.67816092 0.62643678 0.65891473 0.45219638]
----------------------------------------
Trial 2780
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71623563 0.5704023 0.68606322 0.59431525 0.44250646]
----------------------------------------
Trial 2781
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
max_features='log2',
n_estimators=60,
random_state=100))])
cv score: [0.62068966 0.65086207 0.56321839 0.62144703 0.43540052]
----------------------------------------
Trial 2782
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=38,
random_state=100))])
cv score: [0.65948276 0.71695402 0.59626437 0.63953488 0.40568475]
----------------------------------------
Trial 2783
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=91, random_state=100))])
cv score: [0.56321839 0.6875 0.60272989 0.61692506 0.44379845]
----------------------------------------
Trial 2784
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='sqrt',
n_estimators=146, random_state=100,
subsample=0.75))])
cv score: [0.6091954 0.64655172 0.47413793 0.60852713 0.45865633]
----------------------------------------
Trial 2785
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.6954023 0.76436782 0.61627907 0.38888889]
----------------------------------------
Trial 2786
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.69252874 0.56896552 0.53617571 0.42248062]
----------------------------------------
Trial 2787
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.70689655 0.63505747 0.61627907 0.38501292]
----------------------------------------
Trial 2788
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=55,
random_state=100))])
cv score: [0.74712644 0.62931034 0.70402299 0.6498708 0.41085271]
----------------------------------------
Trial 2789
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=176, random_state=100))])
cv score: [0.65373563 0.6408046 0.60775862 0.60594315 0.40180879]
----------------------------------------
Trial 2790
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=104,
random_state=100))])
cv score: [0.61278736 0.67887931 0.63577586 0.70671835 0.38824289]
----------------------------------------
Trial 2791
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.67528736 0.5933908 0.59819121 0.39664083]
----------------------------------------
Trial 2792
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75574713 0.56824713 0.72413793 0.56395349 0.37596899]
----------------------------------------
Trial 2793
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=31,
random_state=100))])
cv score: [0.77155172 0.65086207 0.64439655 0.63049096 0.43863049]
----------------------------------------
Trial 2794
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6566092 0.64224138 0.59482759 0.54651163 0.42377261]
----------------------------------------
Trial 2795
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=40, random_state=100,
subsample=0.7))])
cv score: [0.625 0.39798851 0.31752874 0.44056848 0.53682171]
----------------------------------------
Trial 2796
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=137, random_state=100))])
cv score: [0.73850575 0.65229885 0.67241379 0.65245478 0.43281654]
----------------------------------------
Trial 2797
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=142, random_state=100,
subsample=0.65))])
cv score: [0.61350575 0.49281609 0.54741379 0.58139535 0.48578811]
----------------------------------------
Trial 2798
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='sqrt',
n_estimators=129, random_state=100,
subsample=0.7))])
cv score: [0.62356322 0.59051724 0.7341954 0.62273902 0.40180879]
----------------------------------------
Trial 2799
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=105, random_state=100))])
cv score: [0.55962644 0.7033046 0.61278736 0.60271318 0.43087855]
----------------------------------------
Trial 2800
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.67528736 0.64655172 0.6124031 0.47286822]
----------------------------------------
Trial 2801
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='sqrt',
n_estimators=197, random_state=100,
subsample=0.7))])
cv score: [0.6566092 0.63505747 0.45545977 0.60335917 0.48320413]
----------------------------------------
Trial 2802
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
n_estimators=164, random_state=100,
subsample=0.9))])
cv score: [0.54597701 0.72126437 0.60201149 0.68346253 0.3372093 ]
----------------------------------------
Trial 2803
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=86, random_state=100))])
cv score: [0.62068966 0.6795977 0.61494253 0.67312661 0.42894057]
----------------------------------------
Trial 2804
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=55, random_state=100))])
cv score: [0.74568966 0.63936782 0.70833333 0.53875969 0.36563307]
----------------------------------------
Trial 2805
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=89, random_state=100,
subsample=0.95))])
cv score: [0.64511494 0.67097701 0.53304598 0.65374677 0.48449612]
----------------------------------------
Trial 2806
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51867816 0.71982759 0.62212644 0.63049096 0.37726098]
----------------------------------------
Trial 2807
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=55,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77801724 0.61350575 0.71192529 0.61821705 0.45671835]
----------------------------------------
Trial 2808
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=153,
random_state=100))])
cv score: [0.63721264 0.59913793 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 2809
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68678161 0.65086207 0.62356322 0.61886305 0.39664083]
----------------------------------------
Trial 2810
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='log2',
n_estimators=18, random_state=100,
subsample=0.95))])
cv score: [0.62931034 0.63505747 0.52873563 0.55426357 0.52067183]
----------------------------------------
Trial 2811
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=60,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72198276 0.68821839 0.72988506 0.60852713 0.3875969 ]
----------------------------------------
Trial 2812
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='log2',
n_estimators=166, random_state=100,
subsample=0.7))])
cv score: [0.65229885 0.70545977 0.5862069 0.66925065 0.4754522 ]
----------------------------------------
Trial 2813
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=169,
random_state=100))])
cv score: [0.61781609 0.66666667 0.61781609 0.67700258 0.47157623]
----------------------------------------
Trial 2814
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=162, random_state=100,
subsample=0.7))])
cv score: [0.59051724 0.66091954 0.56465517 0.66795866 0.48062016]
----------------------------------------
Trial 2815
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=68,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7816092 0.67241379 0.67097701 0.63372093 0.46640827]
----------------------------------------
Trial 2816
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=102,
random_state=100))])
cv score: [0.66810345 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 2817
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
n_estimators=160, random_state=100,
subsample=0.65))])
cv score: [0.62787356 0.6637931 0.53017241 0.71576227 0.44702842]
----------------------------------------
Trial 2818
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=168, random_state=100))])
cv score: [0.7112069 0.63649425 0.65373563 0.59560724 0.41472868]
----------------------------------------
Trial 2819
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=97,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79525862 0.55962644 0.70833333 0.59560724 0.41666667]
----------------------------------------
Trial 2820
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=35,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72557471 0.58764368 0.61781609 0.55813953 0.39276486]
----------------------------------------
Trial 2821
----------------------------------------
Parameters {'xgb__n_estimators': 115, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=115,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5933908 0.73275862 0.61206897 0.50904393 0.44315245]
----------------------------------------
Trial 2822
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=151, random_state=100,
subsample=0.9))])
cv score: [0.57183908 0.71695402 0.4683908 0.62015504 0.43023256]
----------------------------------------
Trial 2823
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=76, random_state=100,
subsample=0.8))])
cv score: [0.5933908 0.74712644 0.58908046 0.67183463 0.34366925]
----------------------------------------
Trial 2824
----------------------------------------
Parameters {'xgb__n_estimators': 160, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=160,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73132184 0.6637931 0.68103448 0.64728682 0.41860465]
----------------------------------------
Trial 2825
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=66, random_state=100))])
cv score: [0.63936782 0.63793103 0.49281609 0.66020672 0.38888889]
----------------------------------------
Trial 2826
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=189,
random_state=100))])
cv score: [0.64008621 0.6012931 0.56321839 0.51744186 0.41860465]
----------------------------------------
Trial 2827
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=134,
random_state=100))])
cv score: [0.57614943 0.70402299 0.42097701 0.56072351 0.4121447 ]
----------------------------------------
Trial 2828
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=39,
random_state=100))])
cv score: [0.79382184 0.62571839 0.70402299 0.66343669 0.41085271]
----------------------------------------
Trial 2829
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=134,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52442529 0.67816092 0.5316092 0.53617571 0.34754522]
----------------------------------------
Trial 2830
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=126,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7183908 0.64367816 0.6795977 0.6369509 0.47416021]
----------------------------------------
Trial 2831
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=134,
random_state=100))])
cv score: [0.77155172 0.63362069 0.6716954 0.64082687 0.39147287]
----------------------------------------
Trial 2832
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='sqrt',
n_estimators=13,
random_state=100))])
cv score: [0.55747126 0.61350575 0.4295977 0.54651163 0.47674419]
----------------------------------------
Trial 2833
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='sqrt',
n_estimators=23, random_state=100,
subsample=0.75))])
cv score: [0.69827586 0.67385057 0.4454023 0.65891473 0.45090439]
----------------------------------------
Trial 2834
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=177,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78663793 0.62643678 0.71264368 0.625323 0.44250646]
----------------------------------------
Trial 2835
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=195, random_state=100))])
cv score: [0.60057471 0.73491379 0.64798851 0.68087855 0.43540052]
----------------------------------------
Trial 2836
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=142, random_state=100,
subsample=0.65))])
cv score: [0.72270115 0.7183908 0.60488506 0.68087855 0.50387597]
----------------------------------------
Trial 2837
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=41, random_state=100,
subsample=0.8))])
cv score: [0.70258621 0.60344828 0.70833333 0.59560724 0.45865633]
----------------------------------------
Trial 2838
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=106,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65517241 0.625 0.62068966 0.62919897 0.43927649]
----------------------------------------
Trial 2839
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=62,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80531609 0.56393678 0.67025862 0.60788114 0.48255814]
----------------------------------------
Trial 2840
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=11,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75431034 0.60201149 0.69755747 0.55426357 0.47674419]
----------------------------------------
Trial 2841
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=174,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.73275862 0.74856322 0.64147287 0.36821705]
----------------------------------------
Trial 2842
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=173, random_state=100,
subsample=0.75))])
cv score: [0.58117816 0.49712644 0.43390805 0.54521964 0.44702842]
----------------------------------------
Trial 2843
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=148,
random_state=100))])
cv score: [0.57327586 0.59554598 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 2844
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=163, random_state=100,
subsample=0.65))])
cv score: [0.60344828 0.68247126 0.62356322 0.67958656 0.44832041]
----------------------------------------
Trial 2845
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.6795977 0.60632184 0.59431525 0.40956072]
----------------------------------------
Trial 2846
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=89,
random_state=100))])
cv score: [0.70833333 0.64942529 0.67672414 0.65891473 0.39534884]
----------------------------------------
Trial 2847
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=89, random_state=100,
subsample=0.9))])
cv score: [0.6566092 0.64511494 0.63505747 0.69896641 0.38372093]
----------------------------------------
Trial 2848
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=186,
random_state=100))])
cv score: [0.60775862 0.68247126 0.56034483 0.68217054 0.4250646 ]
----------------------------------------
Trial 2849
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=39,
random_state=100))])
cv score: [0.61925287 0.60704023 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 2850
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
n_estimators=198, random_state=100,
subsample=0.8))])
cv score: [0.61494253 0.57902299 0.54454023 0.65116279 0.46382429]
----------------------------------------
Trial 2851
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=176,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.68390805 0.60344828 0.53617571 0.42635659]
----------------------------------------
Trial 2852
----------------------------------------
Parameters {'gb__n_estimators': 178, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=178,
random_state=100))])
cv score: [0.49137931 0.61063218 0.4295977 0.64470284 0.46770026]
----------------------------------------
Trial 2853
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=130,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5704023 0.69252874 0.66810345 0.6369509 0.39922481]
----------------------------------------
Trial 2854
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=135, random_state=100))])
cv score: [0.6408046 0.64367816 0.62931034 0.6873385 0.39405685]
----------------------------------------
Trial 2855
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.57614943 0.47126437 0.52842377 0.41860465]
----------------------------------------
Trial 2856
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=34, random_state=100))])
cv score: [0.57471264 0.70258621 0.65014368 0.58850129 0.48126615]
----------------------------------------
Trial 2857
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 2858
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=63, random_state=100,
subsample=0.65))])
cv score: [0.54454023 0.63362069 0.62068966 0.50775194 0.42248062]
----------------------------------------
Trial 2859
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=142,
random_state=100))])
cv score: [0.77298851 0.63362069 0.68031609 0.65891473 0.40826873]
----------------------------------------
Trial 2860
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='log2',
n_estimators=156, random_state=100,
subsample=0.9))])
cv score: [0.59051724 0.66954023 0.57902299 0.7118863 0.46899225]
----------------------------------------
Trial 2861
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=74, random_state=100))])
cv score: [0.62212644 0.61781609 0.62931034 0.70542636 0.36692506]
----------------------------------------
Trial 2862
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5545977 0.68103448 0.64655172 0.60465116 0.40956072]
----------------------------------------
Trial 2863
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='log2',
n_estimators=146, random_state=100,
subsample=0.65))])
cv score: [0.41163793 0.43390805 0.73850575 0.65245478 0.47609819]
----------------------------------------
Trial 2864
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=132,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 2865
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=150,
random_state=100))])
cv score: [0.64655172 0.6637931 0.59051724 0.64082687 0.43669251]
----------------------------------------
Trial 2866
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='sqrt',
n_estimators=12, random_state=100,
subsample=0.65))])
cv score: [0.53376437 0.64798851 0.66738506 0.60400517 0.46834625]
----------------------------------------
Trial 2867
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=77,
random_state=100))])
cv score: [0.65948276 0.73275862 0.64942529 0.59819121 0.41472868]
----------------------------------------
Trial 2868
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68678161 0.57471264 0.49928161 0.55103359 0.37144703]
----------------------------------------
Trial 2869
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=120, random_state=100,
subsample=0.95))])
cv score: [0.55603448 0.74425287 0.59770115 0.62144703 0.30232558]
----------------------------------------
Trial 2870
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73347701 0.68318966 0.70545977 0.60335917 0.39082687]
----------------------------------------
Trial 2871
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=148,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 2872
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=61,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70833333 0.7183908 0.59051724 0.58914729 0.4121447 ]
----------------------------------------
Trial 2873
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.71695402 0.72701149 0.67571059 0.40956072]
----------------------------------------
Trial 2874
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=136, random_state=100))])
cv score: [0.63362069 0.63649425 0.61925287 0.59173127 0.40439276]
----------------------------------------
Trial 2875
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=34, random_state=100))])
cv score: [0.72126437 0.65948276 0.67816092 0.52390181 0.39405685]
----------------------------------------
Trial 2876
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=191,
random_state=100))])
cv score: [0.71264368 0.65517241 0.67385057 0.64857881 0.43281654]
----------------------------------------
Trial 2877
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=124, random_state=100))])
cv score: [0.60057471 0.69899425 0.65229885 0.6873385 0.42635659]
----------------------------------------
Trial 2878
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=125, random_state=100,
subsample=0.9))])
cv score: [0.57614943 0.69252874 0.53448276 0.64857881 0.39276486]
----------------------------------------
Trial 2879
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7112069 0.60775862 0.69396552 0.63824289 0.45865633]
----------------------------------------
Trial 2880
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=105, random_state=100))])
cv score: [0.77729885 0.63362069 0.62068966 0.62661499 0.40826873]
----------------------------------------
Trial 2881
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=114,
random_state=100))])
cv score: [0.63074713 0.66666667 0.60057471 0.63565891 0.42635659]
----------------------------------------
Trial 2882
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=39,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 2883
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=65,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 2884
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=72, random_state=100,
subsample=0.95))])
cv score: [0.57183908 0.74568966 0.57614943 0.65245478 0.42764858]
----------------------------------------
Trial 2885
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=141, random_state=100,
subsample=0.85))])
cv score: [0.60775862 0.6637931 0.41522989 0.62144703 0.5620155 ]
----------------------------------------
Trial 2886
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=189,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.72701149 0.59195402 0.61111111 0.43152455]
----------------------------------------
Trial 2887
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
n_estimators=92, random_state=100,
subsample=0.9))])
cv score: [0.6408046 0.71408046 0.61781609 0.69379845 0.41085271]
----------------------------------------
Trial 2888
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=150, random_state=100))])
cv score: [0.72126437 0.63936782 0.67385057 0.59302326 0.4005168 ]
----------------------------------------
Trial 2889
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79382184 0.59626437 0.70474138 0.56912145 0.38501292]
----------------------------------------
Trial 2890
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=53, random_state=100,
subsample=0.75))])
cv score: [0.64224138 0.66522989 0.54310345 0.71576227 0.47803618]
----------------------------------------
Trial 2891
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='sqrt',
n_estimators=78, random_state=100,
subsample=0.65))])
cv score: [0.61781609 0.67241379 0.52298851 0.69121447 0.51808786]
----------------------------------------
Trial 2892
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=41, random_state=100))])
cv score: [0.62787356 0.63074713 0.69396552 0.57105943 0.42894057]
----------------------------------------
Trial 2893
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=74,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 2894
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=98, random_state=100,
subsample=0.75))])
cv score: [0.49137931 0.52155172 0.54454023 0.75968992 0.43927649]
----------------------------------------
Trial 2895
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=35, random_state=100,
subsample=0.7))])
cv score: [0.59482759 0.65086207 0.57614943 0.6627907 0.4870801 ]
----------------------------------------
Trial 2896
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
n_estimators=90, random_state=100,
subsample=0.85))])
cv score: [0.61350575 0.74856322 0.51436782 0.62015504 0.45219638]
----------------------------------------
Trial 2897
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=20, random_state=100,
subsample=0.65))])
cv score: [0.59770115 0.72701149 0.56034483 0.66537468 0.52067183]
----------------------------------------
Trial 2898
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.72270115 0.54885057 0.60077519 0.40568475]
----------------------------------------
Trial 2899
----------------------------------------
Parameters {'rf__n_estimators': 179, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=179,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 2900
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=195, random_state=100,
subsample=0.85))])
cv score: [0.5862069 0.7112069 0.44396552 0.63178295 0.37726098]
----------------------------------------
Trial 2901
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7966954 0.62643678 0.70402299 0.62403101 0.44509044]
----------------------------------------
Trial 2902
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=108,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66235632 0.68103448 0.70258621 0.64082687 0.38436693]
----------------------------------------
Trial 2903
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=106, random_state=100,
subsample=0.9))])
cv score: [0.74137931 0.64942529 0.62787356 0.62015504 0.39018088]
----------------------------------------
Trial 2904
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=145, random_state=100))])
cv score: [0.5625 0.68103448 0.61350575 0.5994832 0.4502584 ]
----------------------------------------
Trial 2905
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=113,
random_state=100))])
cv score: [0.65804598 0.67887931 0.75 0.63824289 0.46511628]
----------------------------------------
Trial 2906
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=75,
random_state=100))])
cv score: [0.80028736 0.61925287 0.66882184 0.62209302 0.38372093]
----------------------------------------
Trial 2907
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=155, random_state=100,
subsample=0.9))])
cv score: [0.53448276 0.7341954 0.60775862 0.70284238 0.30749354]
----------------------------------------
Trial 2908
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=168, random_state=100,
subsample=0.6))])
cv score: [0.62356322 0.63074713 0.43821839 0.66666667 0.49870801]
----------------------------------------
Trial 2909
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=176,
random_state=100))])
cv score: [0.62068966 0.59841954 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 2910
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=100,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.6954023 0.61350575 0.60852713 0.43152455]
----------------------------------------
Trial 2911
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.68534483 0.59770115 0.56718346 0.42635659]
----------------------------------------
Trial 2912
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=35, random_state=100,
subsample=0.6))])
cv score: [0.66810345 0.64367816 0.51005747 0.56330749 0.47286822]
----------------------------------------
Trial 2913
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.7))])
cv score: [0.76436782 0.62643678 0.64942529 0.59302326 0.4496124 ]
----------------------------------------
Trial 2914
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.81321839 0.59195402 0.66594828 0.56459948 0.39793282]
----------------------------------------
Trial 2915
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=60, random_state=100,
subsample=0.6))])
cv score: [0.62212644 0.69683908 0.54454023 0.62144703 0.40310078]
----------------------------------------
Trial 2916
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=15, random_state=100,
subsample=0.65))])
cv score: [0.59913793 0.59051724 0.41522989 0.50775194 0.56330749]
----------------------------------------
Trial 2917
----------------------------------------
Parameters {'xgb__n_estimators': 72, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=72,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77801724 0.56465517 0.63433908 0.63824289 0.38178295]
----------------------------------------
Trial 2918
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=40,
random_state=100))])
cv score: [0.64798851 0.62787356 0.65229885 0.60852713 0.44573643]
----------------------------------------
Trial 2919
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=172,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.69252874 0.5387931 0.47286822 0.44832041]
----------------------------------------
Trial 2920
----------------------------------------
Parameters {'xgb__n_estimators': 146, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=146,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.66810345 0.70258621 0.60206718 0.35271318]
----------------------------------------
Trial 2921
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=112, random_state=100))])
cv score: [0.73706897 0.61350575 0.62643678 0.60335917 0.42764858]
----------------------------------------
Trial 2922
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='sqrt',
n_estimators=176, random_state=100,
subsample=0.8))])
cv score: [0.56465517 0.65948276 0.56321839 0.65374677 0.45478036]
----------------------------------------
Trial 2923
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=45,
random_state=100))])
cv score: [0.57471264 0.63793103 0.66810345 0.70478036 0.39018088]
----------------------------------------
Trial 2924
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=50,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.68247126 0.59770115 0.60465116 0.38242894]
----------------------------------------
Trial 2925
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='log2',
n_estimators=188, random_state=100,
subsample=0.9))])
cv score: [0.58908046 0.6795977 0.38218391 0.56589147 0.43927649]
----------------------------------------
Trial 2926
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=74, random_state=100))])
cv score: [0.60201149 0.65229885 0.53520115 0.59689922 0.43023256]
----------------------------------------
Trial 2927
----------------------------------------
Parameters {'xgb__n_estimators': 56, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=56,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77298851 0.56824713 0.49928161 0.52583979 0.37403101]
----------------------------------------
Trial 2928
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=134, random_state=100))])
cv score: [0.73706897 0.64224138 0.66235632 0.58656331 0.4121447 ]
----------------------------------------
Trial 2929
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=15,
random_state=100))])
cv score: [0.67241379 0.60057471 0.58477011 0.64341085 0.48901809]
----------------------------------------
Trial 2930
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=39,
random_state=100,
subsample=0.85))])
cv score: [0.5316092 0.7112069 0.56896552 0.66408269 0.40956072]
----------------------------------------
Trial 2931
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
n_estimators=32,
random_state=100))])
cv score: [0.63721264 0.6012931 0.61997126 0.46640827 0.42118863]
----------------------------------------
Trial 2932
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=77,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52011494 0.63936782 0.59195402 0.59173127 0.40439276]
----------------------------------------
Trial 2933
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='log2',
n_estimators=113, random_state=100,
subsample=0.95))])
cv score: [0.58908046 0.69252874 0.49856322 0.57235142 0.47674419]
----------------------------------------
Trial 2934
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.73994253 0.66810345 0.56976744 0.39405685]
----------------------------------------
Trial 2935
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
n_estimators=27, random_state=100,
subsample=0.75))])
cv score: [0.54022989 0.57614943 0.57471264 0.45865633 0.45219638]
----------------------------------------
Trial 2936
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=191,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 2937
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=95,
random_state=100))])
cv score: [0.75431034 0.63649425 0.70833333 0.6498708 0.40310078]
----------------------------------------
Trial 2938
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=83, random_state=100,
subsample=0.95))])
cv score: [0.66954023 0.70833333 0.65229885 0.64857881 0.34625323]
----------------------------------------
Trial 2939
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=137,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.72413793 0.62356322 0.53875969 0.40310078]
----------------------------------------
Trial 2940
----------------------------------------
Parameters {'gb__n_estimators': 74, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=74, random_state=100,
subsample=0.9))])
cv score: [0.5316092 0.6795977 0.61206897 0.61369509 0.55167959]
----------------------------------------
Trial 2941
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=148,
random_state=100))])
cv score: [0.58477011 0.68390805 0.58333333 0.6873385 0.41343669]
----------------------------------------
Trial 2942
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=43, random_state=100,
subsample=0.85))])
cv score: [0.41810345 0.63218391 0.5316092 0.64728682 0.36046512]
----------------------------------------
Trial 2943
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=51,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56034483 0.72701149 0.65948276 0.61886305 0.3875969 ]
----------------------------------------
Trial 2944
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=40, random_state=100,
subsample=0.85))])
cv score: [0.61925287 0.66810345 0.63074713 0.64728682 0.44444444]
----------------------------------------
Trial 2945
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=10, random_state=100))])
cv score: [0.77011494 0.62643678 0.34123563 0.62144703 0.39341085]
----------------------------------------
Trial 2946
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71408046 0.60344828 0.69827586 0.6369509 0.4748062 ]
----------------------------------------
Trial 2947
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=110,
random_state=100,
subsample=0.75))])
cv score: [0.54956897 0.60344828 0.58333333 0.31459948 0.48449612]
----------------------------------------
Trial 2948
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=125,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.70833333 0.61494253 0.59431525 0.45478036]
----------------------------------------
Trial 2949
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.72126437 0.60775862 0.65373563 0.60852713 0.41989664]
----------------------------------------
Trial 2950
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
n_estimators=114, random_state=100,
subsample=0.7))])
cv score: [0.54597701 0.43965517 0.61925287 0.65374677 0.51033592]
----------------------------------------
Trial 2951
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=150,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.69827586 0.60201149 0.62919897 0.40439276]
----------------------------------------
Trial 2952
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=53,
random_state=100))])
cv score: [0.58477011 0.63362069 0.64295977 0.66860465 0.39599483]
----------------------------------------
Trial 2953
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=156,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70905172 0.57614943 0.67241379 0.54780362 0.39405685]
----------------------------------------
Trial 2954
----------------------------------------
Parameters {'gb__n_estimators': 165, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=165,
random_state=100))])
cv score: [0.55747126 0.7183908 0.55316092 0.66537468 0.45090439]
----------------------------------------
Trial 2955
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.7))])
cv score: [0.4295977 0.57758621 0.41810345 0.49483204 0.39018088]
----------------------------------------
Trial 2956
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7737069 0.58548851 0.63218391 0.57751938 0.39082687]
----------------------------------------
Trial 2957
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=48,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59626437 0.64798851 0.59913793 0.67312661 0.44702842]
----------------------------------------
Trial 2958
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=46, random_state=100,
subsample=0.7))])
cv score: [0.59913793 0.44971264 0.55316092 0.61498708 0.4005168 ]
----------------------------------------
Trial 2959
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=170, random_state=100,
subsample=0.65))])
cv score: [0.61206897 0.6566092 0.50574713 0.66020672 0.45219638]
----------------------------------------
Trial 2960
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=187, random_state=100))])
cv score: [0.72701149 0.625 0.65086207 0.60723514 0.41989664]
----------------------------------------
Trial 2961
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=111,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7112069 0.68103448 0.66954023 0.63178295 0.4373385 ]
----------------------------------------
Trial 2962
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='log2',
n_estimators=143, random_state=100,
subsample=0.95))])
cv score: [0.6566092 0.66091954 0.50718391 0.55943152 0.55426357]
----------------------------------------
Trial 2963
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.67097701 0.59482759 0.55297158 0.45090439]
----------------------------------------
Trial 2964
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=178,
random_state=100))])
cv score: [0.65948276 0.64511494 0.61925287 0.65503876 0.44702842]
----------------------------------------
Trial 2965
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=66, random_state=100))])
cv score: [0.61925287 0.62356322 0.62068966 0.65762274 0.3630491 ]
----------------------------------------
Trial 2966
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.77586207 0.62068966 0.62661499 0.44573643]
----------------------------------------
Trial 2967
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=56,
random_state=100))])
cv score: [0.60775862 0.62859195 0.64367816 0.67894057 0.3875969 ]
----------------------------------------
Trial 2968
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=24,
random_state=100))])
cv score: [0.6795977 0.6170977 0.5158046 0.70994832 0.45930233]
----------------------------------------
Trial 2969
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.61925287 0.66954023 0.64341085 0.39664083]
----------------------------------------
Trial 2970
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7341954 0.66522989 0.67528736 0.60335917 0.47416021]
----------------------------------------
Trial 2971
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=60,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.57327586 0.47126437 0.53682171 0.35465116]
----------------------------------------
Trial 2972
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=105,
random_state=100))])
cv score: [0.71264368 0.64798851 0.66954023 0.65633075 0.40439276]
----------------------------------------
Trial 2973
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=199,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.72701149 0.5933908 0.5374677 0.41731266]
----------------------------------------
Trial 2974
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=29, random_state=100,
subsample=0.8))])
cv score: [0.54022989 0.39511494 0.54741379 0.59560724 0.42894057]
----------------------------------------
Trial 2975
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=68, random_state=100,
subsample=0.7))])
cv score: [0.55316092 0.54022989 0.43965517 0.67312661 0.39664083]
----------------------------------------
Trial 2976
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=114, random_state=100,
subsample=0.9))])
cv score: [0.78448276 0.60488506 0.73132184 0.6369509 0.36563307]
----------------------------------------
Trial 2977
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=103,
random_state=100))])
cv score: [0.70977011 0.64798851 0.67241379 0.65374677 0.40826873]
----------------------------------------
Trial 2978
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=27,
random_state=100))])
cv score: [0.61853448 0.60344828 0.56824713 0.57105943 0.45348837]
----------------------------------------
Trial 2979
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58764368 0.68247126 0.71551724 0.63953488 0.42054264]
----------------------------------------
Trial 2980
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=34, random_state=100,
subsample=0.6))])
cv score: [0.74425287 0.65086207 0.54741379 0.66925065 0.4121447 ]
----------------------------------------
Trial 2981
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
n_estimators=59, random_state=100,
subsample=0.9))])
cv score: [0.58908046 0.5704023 0.39942529 0.57235142 0.46770026]
----------------------------------------
Trial 2982
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=75,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.65086207 0.6637931 0.59302326 0.41085271]
----------------------------------------
Trial 2983
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=30, random_state=100,
subsample=0.7))])
cv score: [0.64224138 0.49712644 0.57183908 0.65633075 0.36046512]
----------------------------------------
Trial 2984
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=95,
random_state=100))])
cv score: [0.61637931 0.59913793 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 2985
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=22, random_state=100))])
cv score: [0.72270115 0.67241379 0.67816092 0.51614987 0.4496124 ]
----------------------------------------
Trial 2986
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=190,
random_state=100))])
cv score: [0.77729885 0.62787356 0.69037356 0.64082687 0.40956072]
----------------------------------------
Trial 2987
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=35,
random_state=100,
subsample=0.75))])
cv score: [0.54885057 0.68821839 0.52729885 0.63436693 0.42894057]
----------------------------------------
Trial 2988
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=47, random_state=100,
subsample=0.65))])
cv score: [0.68247126 0.60344828 0.64798851 0.62919897 0.53359173]
----------------------------------------
Trial 2989
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=118,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 2990
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=69,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75862069 0.59051724 0.72198276 0.57428941 0.42377261]
----------------------------------------
Trial 2991
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=127,
random_state=100))])
cv score: [0.5545977 0.65876437 0.59195402 0.73901809 0.42571059]
----------------------------------------
Trial 2992
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=139, random_state=100,
subsample=0.85))])
cv score: [0.5704023 0.64511494 0.47413793 0.62015504 0.46899225]
----------------------------------------
Trial 2993
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=27, random_state=100))])
cv score: [0.67097701 0.70402299 0.55316092 0.65568475 0.45736434]
----------------------------------------
Trial 2994
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='sqrt',
n_estimators=123,
random_state=100))])
cv score: [0.53017241 0.64511494 0.4612069 0.60723514 0.49741602]
----------------------------------------
Trial 2995
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73850575 0.57327586 0.65014368 0.60335917 0.40956072]
----------------------------------------
Trial 2996
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=32,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72054598 0.58692529 0.69899425 0.55684755 0.40762274]
----------------------------------------
Trial 2997
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='log2',
n_estimators=157, random_state=100,
subsample=0.7))])
cv score: [0.57327586 0.62068966 0.4841954 0.63565891 0.47674419]
----------------------------------------
Trial 2998
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.61781609 0.61781609 0.61369509 0.42377261]
----------------------------------------
Trial 2999
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=91,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.71695402 0.60775862 0.62015504 0.40826873]
----------------------------------------
Trial 3000
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=14,
random_state=100))])
cv score: [0.54022989 0.64798851 0.61063218 0.50129199 0.54651163]
----------------------------------------
Trial 3001
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=173, random_state=100,
subsample=0.65))])
cv score: [0.56609195 0.69971264 0.55603448 0.71576227 0.44702842]
----------------------------------------
Trial 3002
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='sqrt',
n_estimators=129, random_state=100,
subsample=0.7))])
cv score: [0.61063218 0.63649425 0.5933908 0.68604651 0.45736434]
----------------------------------------
Trial 3003
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74497126 0.59698276 0.65517241 0.56072351 0.41020672]
----------------------------------------
Trial 3004
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=162, random_state=100,
subsample=0.95))])
cv score: [0.59626437 0.71408046 0.56465517 0.62015504 0.40180879]
----------------------------------------
Trial 3005
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77227011 0.58117816 0.67672414 0.63372093 0.53229974]
----------------------------------------
Trial 3006
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65229885 0.65517241 0.60057471 0.60465116 0.42764858]
----------------------------------------
Trial 3007
----------------------------------------
Parameters {'xgb__n_estimators': 171, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=171,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80890805 0.60272989 0.66666667 0.62661499 0.46124031]
----------------------------------------
Trial 3008
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=137, random_state=100))])
cv score: [0.77298851 0.63936782 0.59051724 0.63436693 0.38953488]
----------------------------------------
Trial 3009
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=173, random_state=100))])
cv score: [0.63218391 0.65373563 0.62212644 0.67571059 0.39534884]
----------------------------------------
Trial 3010
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=40,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.59195402 0.52586207 0.67054264 0.44186047]
----------------------------------------
Trial 3011
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=144,
random_state=100))])
cv score: [0.64224138 0.66235632 0.59051724 0.64599483 0.43669251]
----------------------------------------
Trial 3012
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=91, random_state=100))])
cv score: [0.74856322 0.62787356 0.66954023 0.62015504 0.38372093]
----------------------------------------
Trial 3013
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
n_estimators=197, random_state=100,
subsample=0.95))])
cv score: [0.6795977 0.66738506 0.53735632 0.60788114 0.42894057]
----------------------------------------
Trial 3014
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=132, random_state=100))])
cv score: [0.71695402 0.60488506 0.6637931 0.60335917 0.41472868]
----------------------------------------
Trial 3015
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=42,
random_state=100))])
cv score: [0.65948276 0.60201149 0.59626437 0.5620155 0.374677 ]
----------------------------------------
Trial 3016
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=34, random_state=100,
subsample=0.9))])
cv score: [0.66522989 0.67241379 0.47557471 0.68863049 0.49224806]
----------------------------------------
Trial 3017
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=172, random_state=100))])
cv score: [0.7112069 0.63362069 0.64367816 0.59302326 0.40697674]
----------------------------------------
Trial 3018
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=94, random_state=100,
subsample=0.75))])
cv score: [0.70977011 0.66666667 0.5625 0.59366925 0.36563307]
----------------------------------------
Trial 3019
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='log2',
n_estimators=87, random_state=100,
subsample=0.75))])
cv score: [0.67241379 0.69396552 0.56752874 0.66020672 0.42635659]
----------------------------------------
Trial 3020
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71623563 0.56178161 0.63577586 0.55232558 0.38824289]
----------------------------------------
Trial 3021
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=95,
random_state=100))])
cv score: [0.59051724 0.69109195 0.61781609 0.68475452 0.39664083]
----------------------------------------
Trial 3022
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
n_estimators=198,
random_state=100))])
cv score: [0.65158046 0.71192529 0.65445402 0.67183463 0.43087855]
----------------------------------------
Trial 3023
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=154, random_state=100,
subsample=0.85))])
cv score: [0.61206897 0.68103448 0.52298851 0.59689922 0.50645995]
----------------------------------------
Trial 3024
----------------------------------------
Parameters {'rf__n_estimators': 129, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=129,
random_state=100))])
cv score: [0.77155172 0.63074713 0.66163793 0.62919897 0.38888889]
----------------------------------------
Trial 3025
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=198,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.70977011 0.67241379 0.5878553 0.39664083]
----------------------------------------
Trial 3026
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='sqrt',
n_estimators=61, random_state=100,
subsample=0.7))])
cv score: [0.7112069 0.72413793 0.70689655 0.57105943 0.62919897]
----------------------------------------
Trial 3027
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=185,
random_state=100))])
cv score: [0.66522989 0.64511494 0.61350575 0.65245478 0.45090439]
----------------------------------------
Trial 3028
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=39,
random_state=100))])
cv score: [0.57255747 0.60344828 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 3029
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65517241 0.66810345 0.63505747 0.63307494 0.39793282]
----------------------------------------
Trial 3030
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=140,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.46551724 0.70833333 0.61350575 0.55813953 0.45219638]
----------------------------------------
Trial 3031
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='sqrt',
n_estimators=21,
random_state=100))])
cv score: [0.64798851 0.70258621 0.48563218 0.74289406 0.45607235]
----------------------------------------
Trial 3032
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=140, random_state=100))])
cv score: [0.7341954 0.63649425 0.65804598 0.64082687 0.4121447 ]
----------------------------------------
Trial 3033
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=190,
random_state=100))])
cv score: [0.56321839 0.61063218 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 3034
----------------------------------------
Parameters {'rf__n_estimators': 81, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=81,
random_state=100))])
cv score: [0.60344828 0.67816092 0.59482759 0.71059432 0.37596899]
----------------------------------------
Trial 3035
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=117,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.71264368 0.59051724 0.61111111 0.39405685]
----------------------------------------
Trial 3036
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='sqrt',
n_estimators=162, random_state=100,
subsample=0.8))])
cv score: [0.61925287 0.69252874 0.45977011 0.57364341 0.41085271]
----------------------------------------
Trial 3037
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.82183908 0.56752874 0.64727011 0.58268734 0.48901809]
----------------------------------------
Trial 3038
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='sqrt',
n_estimators=18, random_state=100,
subsample=0.75))])
cv score: [0.56896552 0.65373563 0.48994253 0.5 0.52713178]
----------------------------------------
Trial 3039
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72270115 0.6637931 0.71408046 0.65116279 0.38630491]
----------------------------------------
Trial 3040
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76867816 0.66091954 0.6637931 0.59173127 0.44509044]
----------------------------------------
Trial 3041
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=53, random_state=100,
subsample=0.9))])
cv score: [0.70977011 0.63505747 0.66666667 0.60206718 0.44186047]
----------------------------------------
Trial 3042
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74784483 0.64439655 0.64295977 0.71963824 0.39793282]
----------------------------------------
Trial 3043
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='sqrt',
n_estimators=108, random_state=100,
subsample=0.75))])
cv score: [0.67097701 0.66235632 0.61063218 0.65891473 0.4379845 ]
----------------------------------------
Trial 3044
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=77, random_state=100,
subsample=0.85))])
cv score: [0.70402299 0.68390805 0.62356322 0.6124031 0.49612403]
----------------------------------------
Trial 3045
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='sqrt',
n_estimators=117, random_state=100,
subsample=0.8))])
cv score: [0.60344828 0.69827586 0.55316092 0.60981912 0.43410853]
----------------------------------------
Trial 3046
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=61,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73491379 0.57974138 0.7191092 0.67635659 0.42054264]
----------------------------------------
Trial 3047
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.69109195 0.65373563 0.61757106 0.39147287]
----------------------------------------
Trial 3048
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=32,
random_state=100))])
cv score: [0.7183908 0.62643678 0.5933908 0.62144703 0.36950904]
----------------------------------------
Trial 3049
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=162,
random_state=100))])
cv score: [0.61350575 0.66810345 0.62068966 0.67958656 0.46899225]
----------------------------------------
Trial 3050
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7579023 0.66954023 0.7033046 0.60465116 0.41731266]
----------------------------------------
Trial 3051
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=157,
random_state=100))])
cv score: [0.61637931 0.67097701 0.60488506 0.6744186 0.47416021]
----------------------------------------
Trial 3052
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='log2',
n_estimators=92, random_state=100,
subsample=0.65))])
cv score: [0.59195402 0.56896552 0.53304598 0.62403101 0.5129199 ]
----------------------------------------
Trial 3053
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=102,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.70689655 0.59626437 0.60335917 0.44573643]
----------------------------------------
Trial 3054
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=120,
random_state=100))])
cv score: [0.61063218 0.68534483 0.61063218 0.71059432 0.42700258]
----------------------------------------
Trial 3055
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=190, random_state=100,
subsample=0.7))])
cv score: [0.66666667 0.63505747 0.52729885 0.70542636 0.45478036]
----------------------------------------
Trial 3056
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65948276 0.68247126 0.67672414 0.65116279 0.40956072]
----------------------------------------
Trial 3057
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=155,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.69827586 0.68534483 0.58656331 0.43410853]
----------------------------------------
Trial 3058
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=170,
random_state=100))])
cv score: [0.64008621 0.59985632 0.56034483 0.51744186 0.41860465]
----------------------------------------
Trial 3059
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=107,
random_state=100))])
cv score: [0.61350575 0.59770115 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 3060
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=45, random_state=100,
subsample=0.7))])
cv score: [0.73275862 0.59913793 0.66954023 0.64470284 0.43669251]
----------------------------------------
Trial 3061
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.7))])
cv score: [0.68678161 0.63362069 0.48994253 0.61627907 0.45994832]
----------------------------------------
Trial 3062
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='log2',
n_estimators=70,
random_state=100))])
cv score: [0.64942529 0.69396552 0.47988506 0.57751938 0.46382429]
----------------------------------------
Trial 3063
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.71982759 0.70114943 0.60723514 0.46899225]
----------------------------------------
Trial 3064
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75933908 0.64439655 0.68534483 0.61886305 0.46834625]
----------------------------------------
Trial 3065
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=106, random_state=100))])
cv score: [0.55962644 0.70689655 0.61206897 0.60271318 0.42958656]
----------------------------------------
Trial 3066
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=78, random_state=100,
subsample=0.6))])
cv score: [0.65948276 0.59626437 0.59626437 0.64857881 0.51679587]
----------------------------------------
Trial 3067
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=48, random_state=100,
subsample=0.95))])
cv score: [0.60632184 0.71982759 0.61781609 0.60077519 0.40891473]
----------------------------------------
Trial 3068
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51724138 0.70689655 0.72701149 0.61563307 0.40180879]
----------------------------------------
Trial 3069
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=162, random_state=100))])
cv score: [0.60775862 0.69971264 0.66666667 0.69509044 0.44056848]
----------------------------------------
Trial 3070
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=88,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54166667 0.71551724 0.70905172 0.61692506 0.3869509 ]
----------------------------------------
Trial 3071
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=20, random_state=100))])
cv score: [0.75431034 0.60488506 0.6091954 0.60788114 0.48191214]
----------------------------------------
Trial 3072
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73850575 0.55100575 0.64152299 0.59754522 0.375323 ]
----------------------------------------
Trial 3073
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=187, random_state=100,
subsample=0.95))])
cv score: [0.64511494 0.66666667 0.52298851 0.66925065 0.44315245]
----------------------------------------
Trial 3074
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=106,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77586207 0.57758621 0.70114943 0.62209302 0.48578811]
----------------------------------------
Trial 3075
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=193, random_state=100))])
cv score: [0.72413793 0.6408046 0.63793103 0.60981912 0.40956072]
----------------------------------------
Trial 3076
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=170,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77729885 0.59770115 0.70402299 0.59431525 0.44121447]
----------------------------------------
Trial 3077
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=161,
random_state=100))])
cv score: [0.5862069 0.68175287 0.61997126 0.71576227 0.39534884]
----------------------------------------
Trial 3078
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=142,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.69683908 0.59482759 0.65374677 0.43927649]
----------------------------------------
Trial 3079
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=24, random_state=100,
subsample=0.85))])
cv score: [0.6566092 0.67816092 0.60488506 0.70155039 0.41860465]
----------------------------------------
Trial 3080
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
n_estimators=169, random_state=100,
subsample=0.8))])
cv score: [0.56752874 0.71982759 0.61925287 0.67958656 0.41472868]
----------------------------------------
Trial 3081
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=143,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65517241 0.65804598 0.64224138 0.6124031 0.4496124 ]
----------------------------------------
Trial 3082
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=183, random_state=100,
subsample=0.95))])
cv score: [0.57758621 0.70689655 0.50143678 0.64341085 0.45865633]
----------------------------------------
Trial 3083
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=38, random_state=100,
subsample=0.65))])
cv score: [0.56896552 0.65804598 0.63649425 0.66925065 0.35658915]
----------------------------------------
Trial 3084
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=48, random_state=100,
subsample=0.6))])
cv score: [0.62356322 0.56752874 0.77442529 0.46770026 0.42700258]
----------------------------------------
Trial 3085
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=167,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72126437 0.61925287 0.69683908 0.5994832 0.45478036]
----------------------------------------
Trial 3086
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=81, random_state=100,
subsample=0.8))])
cv score: [0.56752874 0.57758621 0.60488506 0.625323 0.4250646 ]
----------------------------------------
Trial 3087
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=43, random_state=100,
subsample=0.9))])
cv score: [0.60632184 0.70402299 0.50718391 0.56976744 0.46382429]
----------------------------------------
Trial 3088
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=36,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71982759 0.58764368 0.60344828 0.58850129 0.48578811]
----------------------------------------
Trial 3089
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.63362069 0.63218391 0.64511494 0.59043928 0.40956072]
----------------------------------------
Trial 3090
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=43,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76221264 0.54956897 0.61422414 0.62338501 0.4870801 ]
----------------------------------------
Trial 3091
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=27, random_state=100,
subsample=0.7))])
cv score: [0.70258621 0.65229885 0.65373563 0.68669251 0.3998708 ]
----------------------------------------
Trial 3092
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=34, random_state=100,
subsample=0.65))])
cv score: [0.58333333 0.65086207 0.57614943 0.6130491 0.57881137]
----------------------------------------
Trial 3093
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=181, random_state=100,
subsample=0.75))])
cv score: [0.57327586 0.5316092 0.41091954 0.36563307 0.51937984]
----------------------------------------
Trial 3094
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=19, random_state=100))])
cv score: [0.66666667 0.63218391 0.51221264 0.60852713 0.3501292 ]
----------------------------------------
Trial 3095
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=22,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70761494 0.59482759 0.6716954 0.58527132 0.4250646 ]
----------------------------------------
Trial 3096
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.69396552 0.64511494 0.62661499 0.41989664]
----------------------------------------
Trial 3097
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=176,
random_state=100))])
cv score: [0.60344828 0.68821839 0.56465517 0.68346253 0.43023256]
----------------------------------------
Trial 3098
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
n_estimators=182, random_state=100,
subsample=0.95))])
cv score: [0.61494253 0.70689655 0.59626437 0.71447028 0.41989664]
----------------------------------------
Trial 3099
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=86, random_state=100,
subsample=0.85))])
cv score: [0.62212644 0.64942529 0.54166667 0.625323 0.37338501]
----------------------------------------
Trial 3100
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=179,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.69971264 0.7112069 0.6375969 0.39147287]
----------------------------------------
Trial 3101
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=36, random_state=100))])
cv score: [0.5466954 0.66091954 0.54741379 0.63178295 0.46576227]
----------------------------------------
Trial 3102
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=149, random_state=100,
subsample=0.7))])
cv score: [0.51436782 0.69827586 0.54741379 0.70413437 0.43540052]
----------------------------------------
Trial 3103
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=144,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67600575 0.58477011 0.63218391 0.53488372 0.37209302]
----------------------------------------
Trial 3104
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=58, random_state=100,
subsample=0.75))])
cv score: [0.54022989 0.70545977 0.55028736 0.68346253 0.4379845 ]
----------------------------------------
Trial 3105
----------------------------------------
Parameters {'rf__n_estimators': 125, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=125, random_state=100))])
cv score: [0.61063218 0.70689655 0.65229885 0.68863049 0.42054264]
----------------------------------------
Trial 3106
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
n_estimators=27, random_state=100,
subsample=0.85))])
cv score: [0.51005747 0.67241379 0.54310345 0.56718346 0.48578811]
----------------------------------------
Trial 3107
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=160, random_state=100,
subsample=0.7))])
cv score: [0.61637931 0.44683908 0.64511494 0.63953488 0.4121447 ]
----------------------------------------
Trial 3108
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.69109195 0.57471264 0.58397933 0.44186047]
----------------------------------------
Trial 3109
----------------------------------------
Parameters {'xgb__n_estimators': 124, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=124,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.67385057 0.60344828 0.60077519 0.44702842]
----------------------------------------
Trial 3110
----------------------------------------
Parameters {'gb__n_estimators': 82, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, n_estimators=82,
random_state=100, subsample=0.8))])
cv score: [0.63505747 0.71982759 0.65086207 0.63953488 0.46124031]
----------------------------------------
Trial 3111
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=97,
random_state=100))])
cv score: [0.61278736 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 3112
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.64224138 0.61494253 0.4754522 0.40697674]
----------------------------------------
Trial 3113
----------------------------------------
Parameters {'xgb__n_estimators': 99, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=99,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.6408046 0.74568966 0.6621447 0.42312661]
----------------------------------------
Trial 3114
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=142,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.70833333 0.61637931 0.54780362 0.4005168 ]
----------------------------------------
Trial 3115
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=13, random_state=100,
subsample=0.7))])
cv score: [0.49568966 0.63074713 0.53017241 0.6498708 0.43217054]
----------------------------------------
Trial 3116
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
n_estimators=62, random_state=100,
subsample=0.9))])
cv score: [0.7033046 0.67313218 0.63577586 0.625323 0.43152455]
----------------------------------------
Trial 3117
----------------------------------------
Parameters {'gb__n_estimators': 93, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=93, random_state=100,
subsample=0.95))])
cv score: [0.57902299 0.56178161 0.51293103 0.66149871 0.37984496]
----------------------------------------
Trial 3118
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=70, random_state=100))])
cv score: [0.7183908 0.60775862 0.57614943 0.56589147 0.41602067]
----------------------------------------
Trial 3119
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=136, random_state=100,
subsample=0.75))])
cv score: [0.63074713 0.68390805 0.60057471 0.66408269 0.43669251]
----------------------------------------
Trial 3120
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.8))])
cv score: [0.57327586 0.59770115 0.5 0.56847545 0.38630491]
----------------------------------------
Trial 3121
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72557471 0.56321839 0.64942529 0.57041344 0.39147287]
----------------------------------------
Trial 3122
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=40,
random_state=100))])
cv score: [0.5941092 0.66307471 0.65373563 0.65826873 0.42118863]
----------------------------------------
Trial 3123
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.70545977 0.53591954 0.61111111 0.40439276]
----------------------------------------
Trial 3124
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='sqrt',
n_estimators=199, random_state=100,
subsample=0.6))])
cv score: [0.76436782 0.66522989 0.6637931 0.61111111 0.4250646 ]
----------------------------------------
Trial 3125
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=169, random_state=100,
subsample=0.65))])
cv score: [0.61063218 0.67097701 0.4137931 0.48901809 0.48191214]
----------------------------------------
Trial 3126
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=131, random_state=100))])
cv score: [0.61925287 0.70689655 0.66954023 0.67312661 0.42829457]
----------------------------------------
Trial 3127
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=97,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.75287356 0.54597701 0.58139535 0.45348837]
----------------------------------------
Trial 3128
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=160,
random_state=100))])
cv score: [0.61494253 0.66954023 0.625 0.67571059 0.47416021]
----------------------------------------
Trial 3129
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=175,
random_state=100))])
cv score: [0.70258621 0.65373563 0.66954023 0.65762274 0.42635659]
----------------------------------------
Trial 3130
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=23, random_state=100))])
cv score: [0.61350575 0.58908046 0.52011494 0.72739018 0.39018088]
----------------------------------------
Trial 3131
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='sqrt',
n_estimators=145, random_state=100,
subsample=0.6))])
cv score: [0.67816092 0.62068966 0.61206897 0.56976744 0.43023256]
----------------------------------------
Trial 3132
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=136,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75143678 0.67816092 0.68965517 0.60788114 0.42829457]
----------------------------------------
Trial 3133
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=102,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77873563 0.58117816 0.65804598 0.61757106 0.44573643]
----------------------------------------
Trial 3134
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=196,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.68247126 0.64367816 0.64728682 0.40180879]
----------------------------------------
Trial 3135
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=56, random_state=100,
subsample=0.8))])
cv score: [0.6795977 0.61925287 0.5158046 0.63565891 0.41343669]
----------------------------------------
Trial 3136
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.68534483 0.66522989 0.60465116 0.39922481]
----------------------------------------
Trial 3137
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80316092 0.58979885 0.70545977 0.6369509 0.34173127]
----------------------------------------
Trial 3138
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='sqrt',
n_estimators=112, random_state=100,
subsample=0.8))])
cv score: [0.53448276 0.63362069 0.48706897 0.60594315 0.51033592]
----------------------------------------
Trial 3139
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=44, random_state=100))])
cv score: [0.61853448 0.70474138 0.64152299 0.67829457 0.44832041]
----------------------------------------
Trial 3140
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='sqrt',
n_estimators=147, random_state=100,
subsample=0.8))])
cv score: [0.60201149 0.67241379 0.52586207 0.68863049 0.42635659]
----------------------------------------
Trial 3141
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=142, random_state=100,
subsample=0.8))])
cv score: [0.59482759 0.63505747 0.54597701 0.62403101 0.44186047]
----------------------------------------
Trial 3142
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=164, random_state=100))])
cv score: [0.62931034 0.65517241 0.625 0.68217054 0.39534884]
----------------------------------------
Trial 3143
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=155,
random_state=100))])
cv score: [0.58908046 0.68103448 0.63649425 0.68087855 0.43410853]
----------------------------------------
Trial 3144
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=164,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.69109195 0.61494253 0.64470284 0.39147287]
----------------------------------------
Trial 3145
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=40, random_state=100))])
cv score: [0.53304598 0.61350575 0.60057471 0.71963824 0.34237726]
----------------------------------------
Trial 3146
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='sqrt',
n_estimators=107, random_state=100,
subsample=0.6))])
cv score: [0.63649425 0.64511494 0.63074713 0.61886305 0.44056848]
----------------------------------------
Trial 3147
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=48, random_state=100,
subsample=0.75))])
cv score: [0.57183908 0.68678161 0.52298851 0.66925065 0.43669251]
----------------------------------------
Trial 3148
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=110,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.66810345 0.54310345 0.52971576 0.48126615]
----------------------------------------
Trial 3149
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='log2',
n_estimators=108, random_state=100,
subsample=0.95))])
cv score: [0.63218391 0.6795977 0.5158046 0.64341085 0.44315245]
----------------------------------------
Trial 3150
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=130, random_state=100,
subsample=0.95))])
cv score: [0.63218391 0.66235632 0.53304598 0.57364341 0.45478036]
----------------------------------------
Trial 3151
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=14,
random_state=100))])
cv score: [0.78017241 0.62931034 0.56609195 0.59625323 0.40310078]
----------------------------------------
Trial 3152
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=53,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73706897 0.56465517 0.66307471 0.64082687 0.39341085]
----------------------------------------
Trial 3153
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=105, random_state=100))])
cv score: [0.68390805 0.6795977 0.60057471 0.63565891 0.41989664]
----------------------------------------
Trial 3154
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=91, random_state=100,
subsample=0.65))])
cv score: [0.45545977 0.625 0.58189655 0.67958656 0.42894057]
----------------------------------------
Trial 3155
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=107,
random_state=100))])
cv score: [0.62212644 0.66091954 0.59913793 0.65374677 0.44702842]
----------------------------------------
Trial 3156
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69755747 0.57974138 0.69899425 0.67248062 0.4244186 ]
----------------------------------------
Trial 3157
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=135,
random_state=100))])
cv score: [0.59626437 0.67600575 0.63936782 0.71705426 0.39534884]
----------------------------------------
Trial 3158
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=69,
random_state=100))])
cv score: [0.77658046 0.6091954 0.68175287 0.62919897 0.38372093]
----------------------------------------
Trial 3159
----------------------------------------
Parameters {'rf__n_estimators': 45, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=45,
random_state=100))])
cv score: [0.57471264 0.63793103 0.66810345 0.70478036 0.39018088]
----------------------------------------
Trial 3160
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='sqrt',
n_estimators=39, random_state=100,
subsample=0.85))])
cv score: [0.72270115 0.72844828 0.45689655 0.6498708 0.47674419]
----------------------------------------
Trial 3161
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.95))])
cv score: [0.59051724 0.68965517 0.41091954 0.6369509 0.35917313]
----------------------------------------
Trial 3162
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.70258621 0.70833333 0.64857881 0.38888889]
----------------------------------------
Trial 3163
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='sqrt',
n_estimators=130, random_state=100,
subsample=0.65))])
cv score: [0.37068966 0.61135057 0.71264368 0.55684755 0.67183463]
----------------------------------------
Trial 3164
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.48994253 0.62931034 0.52729885 0.5374677 0.4870801 ]
----------------------------------------
Trial 3165
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=76,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.69827586 0.67816092 0.63436693 0.45671835]
----------------------------------------
Trial 3166
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=65, random_state=100,
subsample=0.6))])
cv score: [0.68534483 0.60632184 0.59051724 0.60981912 0.45219638]
----------------------------------------
Trial 3167
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=127, random_state=100,
subsample=0.7))])
cv score: [0.55890805 0.70545977 0.5316092 0.69121447 0.46124031]
----------------------------------------
Trial 3168
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.67528736 0.52155172 0.55297158 0.39534884]
----------------------------------------
Trial 3169
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=193,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.63936782 0.55316092 0.54263566 0.45478036]
----------------------------------------
Trial 3170
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=174, random_state=100,
subsample=0.65))])
cv score: [0.71264368 0.64367816 0.64655172 0.64211886 0.45865633]
----------------------------------------
Trial 3171
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.65))])
cv score: [0.65086207 0.57327586 0.43821839 0.68087855 0.40697674]
----------------------------------------
Trial 3172
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='log2',
n_estimators=92, random_state=100,
subsample=0.65))])
cv score: [0.66522989 0.67385057 0.55316092 0.62273902 0.41343669]
----------------------------------------
Trial 3173
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='log2',
n_estimators=30,
random_state=100))])
cv score: [0.66235632 0.67816092 0.74137931 0.63436693 0.48449612]
----------------------------------------
Trial 3174
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=120, random_state=100))])
cv score: [0.60488506 0.70761494 0.63362069 0.66925065 0.41795866]
----------------------------------------
Trial 3175
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
n_estimators=182, random_state=100,
subsample=0.9))])
cv score: [0.6795977 0.69683908 0.64798851 0.64211886 0.41085271]
----------------------------------------
Trial 3176
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=117, random_state=100))])
cv score: [0.6795977 0.68247126 0.5862069 0.63178295 0.41989664]
----------------------------------------
Trial 3177
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=171, random_state=100,
subsample=0.6))])
cv score: [0.66235632 0.70402299 0.66954023 0.66537468 0.46640827]
----------------------------------------
Trial 3178
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=27,
random_state=100))])
cv score: [0.69971264 0.61350575 0.63074713 0.69121447 0.41343669]
----------------------------------------
Trial 3179
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='sqrt',
n_estimators=120, random_state=100,
subsample=0.85))])
cv score: [0.59051724 0.68247126 0.40517241 0.60981912 0.54134367]
----------------------------------------
Trial 3180
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=52,
random_state=100))])
cv score: [0.58764368 0.63649425 0.60991379 0.67312661 0.39793282]
----------------------------------------
Trial 3181
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75862069 0.56465517 0.5941092 0.61498708 0.43992248]
----------------------------------------
Trial 3182
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=100,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65517241 0.74281609 0.5933908 0.5878553 0.43152455]
----------------------------------------
Trial 3183
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
n_estimators=187, random_state=100,
subsample=0.95))])
cv score: [0.49568966 0.60344828 0.52729885 0.74418605 0.3875969 ]
----------------------------------------
Trial 3184
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=59, random_state=100))])
cv score: [0.54238506 0.61278736 0.57614943 0.53875969 0.42054264]
----------------------------------------
Trial 3185
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=127,
random_state=100))])
cv score: [0.77873563 0.63505747 0.67025862 0.6369509 0.40439276]
----------------------------------------
Trial 3186
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=114,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.63649425 0.59195402 0.51937984 0.43281654]
----------------------------------------
Trial 3187
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=75, random_state=100,
subsample=0.6))])
cv score: [0.69252874 0.60488506 0.56178161 0.5374677 0.30490956]
----------------------------------------
Trial 3188
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=138, random_state=100,
subsample=0.65))])
cv score: [0.72126437 0.6637931 0.56465517 0.64728682 0.41989664]
----------------------------------------
Trial 3189
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=87,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70258621 0.62571839 0.68606322 0.64728682 0.47868217]
----------------------------------------
Trial 3190
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=136, random_state=100,
subsample=0.75))])
cv score: [0.50431034 0.61063218 0.41091954 0.64341085 0.46640827]
----------------------------------------
Trial 3191
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=106,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.63505747 0.5387931 0.57881137 0.41343669]
----------------------------------------
Trial 3192
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
n_estimators=64,
random_state=100))])
cv score: [0.70761494 0.65445402 0.4533046 0.61046512 0.40891473]
----------------------------------------
Trial 3193
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=177,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 3194
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.70545977 0.53017241 0.59173127 0.43023256]
----------------------------------------
Trial 3195
----------------------------------------
Parameters {'xgb__n_estimators': 171, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=171,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.73563218 0.59913793 0.63049096 0.39922481]
----------------------------------------
Trial 3196
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=185,
random_state=100))])
cv score: [0.54597701 0.65229885 0.59051724 0.74160207 0.40116279]
----------------------------------------
Trial 3197
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=73,
random_state=100))])
cv score: [0.59051724 0.64152299 0.62356322 0.69379845 0.37984496]
----------------------------------------
Trial 3198
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=34, random_state=100))])
cv score: [0.68103448 0.65876437 0.53735632 0.65891473 0.48062016]
----------------------------------------
Trial 3199
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77801724 0.60272989 0.65158046 0.61111111 0.37273902]
----------------------------------------
Trial 3200
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=32,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54166667 0.70545977 0.68678161 0.61757106 0.37919897]
----------------------------------------
Trial 3201
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
n_estimators=163, random_state=100,
subsample=0.7))])
cv score: [0.56609195 0.69971264 0.54741379 0.69379845 0.46770026]
----------------------------------------
Trial 3202
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=24,
random_state=100))])
cv score: [0.52298851 0.57183908 0.67385057 0.6873385 0.40568475]
----------------------------------------
Trial 3203
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=194, random_state=100))])
cv score: [0.64511494 0.66810345 0.58333333 0.68604651 0.39018088]
----------------------------------------
Trial 3204
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='log2',
n_estimators=107, random_state=100,
subsample=0.85))])
cv score: [0.6637931 0.61781609 0.53017241 0.67829457 0.47157623]
----------------------------------------
Trial 3205
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=175,
random_state=100))])
cv score: [0.72557471 0.65373563 0.58908046 0.63824289 0.42635659]
----------------------------------------
Trial 3206
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=179, random_state=100,
subsample=0.6))])
cv score: [0.66235632 0.65086207 0.56034483 0.63436693 0.42635659]
----------------------------------------
Trial 3207
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='log2',
n_estimators=95, random_state=100,
subsample=0.9))])
cv score: [0.57614943 0.7112069 0.46408046 0.65374677 0.41731266]
----------------------------------------
Trial 3208
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=70,
random_state=100))])
cv score: [0.73275862 0.64655172 0.70545977 0.65245478 0.40826873]
----------------------------------------
Trial 3209
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65014368 0.73132184 0.70617816 0.64470284 0.39664083]
----------------------------------------
Trial 3210
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=58, random_state=100,
subsample=0.65))])
cv score: [0.37068966 0.61135057 0.71264368 0.55684755 0.67183463]
----------------------------------------
Trial 3211
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=71, random_state=100,
subsample=0.75))])
cv score: [0.71408046 0.67097701 0.53376437 0.60271318 0.36434109]
----------------------------------------
Trial 3212
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=155,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.6954023 0.58908046 0.65374677 0.45736434]
----------------------------------------
Trial 3213
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=76, random_state=100,
subsample=0.9))])
cv score: [0.55603448 0.72270115 0.57471264 0.63953488 0.37080103]
----------------------------------------
Trial 3214
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=177, random_state=100))])
cv score: [0.60344828 0.67241379 0.58045977 0.62790698 0.43281654]
----------------------------------------
Trial 3215
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.61206897 0.53304598 0.59819121 0.37855297]
----------------------------------------
Trial 3216
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='sqrt',
n_estimators=114, random_state=100,
subsample=0.65))])
cv score: [0.60201149 0.70258621 0.63074713 0.68217054 0.34496124]
----------------------------------------
Trial 3217
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=189,
random_state=100))])
cv score: [0.5862069 0.66954023 0.62212644 0.68346253 0.41860465]
----------------------------------------
Trial 3218
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.64511494 0.61494253 0.59431525 0.43281654]
----------------------------------------
Trial 3219
----------------------------------------
Parameters {'xgb__n_estimators': 38, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=38,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68390805 0.57758621 0.61925287 0.63565891 0.41149871]
----------------------------------------
Trial 3220
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=107,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.76293103 0.58333333 0.55167959 0.45994832]
----------------------------------------
Trial 3221
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=33,
random_state=100))])
cv score: [0.77873563 0.64224138 0.64152299 0.62015504 0.42958656]
----------------------------------------
Trial 3222
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=106,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.67816092 0.60344828 0.60077519 0.41602067]
----------------------------------------
Trial 3223
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=161, random_state=100))])
cv score: [0.74281609 0.62643678 0.68965517 0.65891473 0.42764858]
----------------------------------------
Trial 3224
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, n_estimators=35,
random_state=100,
subsample=0.85))])
cv score: [0.60201149 0.69683908 0.57902299 0.64341085 0.42635659]
----------------------------------------
Trial 3225
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='sqrt',
n_estimators=25, random_state=100,
subsample=0.75))])
cv score: [0.67097701 0.60632184 0.49712644 0.63436693 0.32041344]
----------------------------------------
Trial 3226
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=124,
random_state=100))])
cv score: [0.77729885 0.63505747 0.66020115 0.63178295 0.38372093]
----------------------------------------
Trial 3227
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.68678161 0.61781609 0.52842377 0.41731266]
----------------------------------------
Trial 3228
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.71982759 0.6795977 0.64599483 0.45413437]
----------------------------------------
Trial 3229
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=113, random_state=100))])
cv score: [0.58477011 0.67097701 0.58764368 0.63565891 0.44186047]
----------------------------------------
Trial 3230
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=118, random_state=100,
subsample=0.8))])
cv score: [0.54597701 0.57902299 0.6408046 0.58914729 0.54134367]
----------------------------------------
Trial 3231
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=158,
random_state=100))])
cv score: [0.75143678 0.65086207 0.68821839 0.63824289 0.44186047]
----------------------------------------
Trial 3232
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79310345 0.59482759 0.63505747 0.68410853 0.50129199]
----------------------------------------
Trial 3233
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=83, random_state=100))])
cv score: [0.73275862 0.62068966 0.66522989 0.5620155 0.38372093]
----------------------------------------
Trial 3234
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=158,
random_state=100))])
cv score: [0.59482759 0.67528736 0.62212644 0.68087855 0.43023256]
----------------------------------------
Trial 3235
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=59, random_state=100,
subsample=0.8))])
cv score: [0.58764368 0.59051724 0.66954023 0.61627907 0.5245478 ]
----------------------------------------
Trial 3236
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=66,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80172414 0.57686782 0.6795977 0.66989664 0.4005168 ]
----------------------------------------
Trial 3237
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73850575 0.65804598 0.70545977 0.69638243 0.4244186 ]
----------------------------------------
Trial 3238
----------------------------------------
Parameters {'rf__n_estimators': 156, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=156, random_state=100))])
cv score: [0.5933908 0.70043103 0.66666667 0.70542636 0.41537468]
----------------------------------------
Trial 3239
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=76,
random_state=100))])
cv score: [0.58836207 0.66594828 0.65948276 0.7125323 0.38113695]
----------------------------------------
Trial 3240
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='log2',
n_estimators=189, random_state=100,
subsample=0.9))])
cv score: [0.70689655 0.65373563 0.64367816 0.66149871 0.4005168 ]
----------------------------------------
Trial 3241
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=53,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56178161 0.71408046 0.76867816 0.62726098 0.41408269]
----------------------------------------
Trial 3242
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
n_estimators=109, random_state=100,
subsample=0.6))])
cv score: [0.61494253 0.5 0.20545977 0.51679587 0.5245478 ]
----------------------------------------
Trial 3243
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.68821839 0.58477011 0.64082687 0.40826873]
----------------------------------------
Trial 3244
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='sqrt',
n_estimators=173, random_state=100,
subsample=0.75))])
cv score: [0.62787356 0.67385057 0.5545977 0.64470284 0.46124031]
----------------------------------------
Trial 3245
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=85,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75574713 0.56896552 0.70186782 0.58462532 0.42248062]
----------------------------------------
Trial 3246
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=91,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74784483 0.59770115 0.70043103 0.56847545 0.39728682]
----------------------------------------
Trial 3247
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=42,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73132184 0.70402299 0.65517241 0.67183463 0.47868217]
----------------------------------------
Trial 3248
----------------------------------------
Parameters {'xgb__n_estimators': 152, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=152,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65229885 0.63649425 0.66666667 0.58397933 0.3875969 ]
----------------------------------------
Trial 3249
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=25,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55316092 0.66810345 0.61637931 0.65116279 0.36175711]
----------------------------------------
Trial 3250
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=45,
random_state=100))])
cv score: [0.44252874 0.61637931 0.52873563 0.76485788 0.39405685]
----------------------------------------
Trial 3251
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=35, random_state=100,
subsample=0.95))])
cv score: [0.71695402 0.70043103 0.47126437 0.62919897 0.41085271]
----------------------------------------
Trial 3252
----------------------------------------
Parameters {'xgb__n_estimators': 171, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=171,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74568966 0.65373563 0.68821839 0.66537468 0.44444444]
----------------------------------------
Trial 3253
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=29, random_state=100,
subsample=0.75))])
cv score: [0.5933908 0.57614943 0.54022989 0.43281654 0.59431525]
----------------------------------------
Trial 3254
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.6795977 0.52155172 0.54521964 0.45865633]
----------------------------------------
Trial 3255
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=125, random_state=100,
subsample=0.85))])
cv score: [0.54741379 0.68103448 0.58477011 0.65762274 0.46382429]
----------------------------------------
Trial 3256
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=94, random_state=100))])
cv score: [0.59626437 0.65804598 0.56321839 0.62273902 0.45348837]
----------------------------------------
Trial 3257
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=137, random_state=100))])
cv score: [0.63936782 0.64367816 0.625 0.68087855 0.39276486]
----------------------------------------
Trial 3258
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72988506 0.58045977 0.63218391 0.57428941 0.40180879]
----------------------------------------
Trial 3259
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=24,
random_state=100))])
cv score: [0.62643678 0.65086207 0.55172414 0.57881137 0.46124031]
----------------------------------------
Trial 3260
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=197,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.67672414 0.61781609 0.59689922 0.42635659]
----------------------------------------
Trial 3261
----------------------------------------
Parameters {'rf__n_estimators': 119, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=119, random_state=100))])
cv score: [0.7341954 0.60344828 0.63936782 0.60981912 0.42118863]
----------------------------------------
Trial 3262
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=150, random_state=100,
subsample=0.6))])
cv score: [0.69971264 0.59626437 0.38362069 0.65116279 0.5 ]
----------------------------------------
Trial 3263
----------------------------------------
Parameters {'rf__n_estimators': 60, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=60,
random_state=100))])
cv score: [0.56321839 0.61206897 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 3264
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=167, random_state=100,
subsample=0.95))])
cv score: [0.68821839 0.70114943 0.72844828 0.62661499 0.43540052]
----------------------------------------
Trial 3265
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80028736 0.57255747 0.69181034 0.61563307 0.49870801]
----------------------------------------
Trial 3266
----------------------------------------
Parameters {'xgb__n_estimators': 173, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=173,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60201149 0.70545977 0.57902299 0.66408269 0.40826873]
----------------------------------------
Trial 3267
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=126,
random_state=100))])
cv score: [0.63505747 0.64798851 0.44827586 0.70155039 0.46124031]
----------------------------------------
Trial 3268
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='log2',
n_estimators=131, random_state=100,
subsample=0.75))])
cv score: [0.66954023 0.70114943 0.47413793 0.54521964 0.49095607]
----------------------------------------
Trial 3269
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='log2',
n_estimators=153, random_state=100,
subsample=0.6))])
cv score: [0.64224138 0.62787356 0.49568966 0.625323 0.46640827]
----------------------------------------
Trial 3270
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=105, random_state=100))])
cv score: [0.58764368 0.65804598 0.59051724 0.61498708 0.43410853]
----------------------------------------
Trial 3271
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.70689655 0.60344828 0.58656331 0.37209302]
----------------------------------------
Trial 3272
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=25,
random_state=100,
subsample=0.85))])
cv score: [0.6091954 0.72557471 0.57902299 0.71705426 0.48966408]
----------------------------------------
Trial 3273
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=178,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.71264368 0.63218391 0.63049096 0.39405685]
----------------------------------------
Trial 3274
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=85,
random_state=100))])
cv score: [0.61637931 0.59913793 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 3275
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.69971264 0.75431034 0.61886305 0.36950904]
----------------------------------------
Trial 3276
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=103, random_state=100,
subsample=0.75))])
cv score: [0.56178161 0.69827586 0.55747126 0.66537468 0.47286822]
----------------------------------------
Trial 3277
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=143, random_state=100,
subsample=0.85))])
cv score: [0.57902299 0.73850575 0.62787356 0.72997416 0.44315245]
----------------------------------------
Trial 3278
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='log2',
n_estimators=186, random_state=100,
subsample=0.75))])
cv score: [0.57614943 0.61925287 0.48563218 0.60206718 0.50129199]
----------------------------------------
Trial 3279
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=44, random_state=100))])
cv score: [0.54094828 0.67744253 0.5862069 0.6001292 0.47351421]
----------------------------------------
Trial 3280
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=102, random_state=100))])
cv score: [0.77729885 0.63505747 0.625 0.625323 0.40826873]
----------------------------------------
Trial 3281
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=134,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68390805 0.63505747 0.64511494 0.61498708 0.4379845 ]
----------------------------------------
Trial 3282
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.6954023 0.59626437 0.60852713 0.42764858]
----------------------------------------
Trial 3283
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=199, random_state=100,
subsample=0.9))])
cv score: [0.63218391 0.68678161 0.57183908 0.69379845 0.42635659]
----------------------------------------
Trial 3284
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=156, random_state=100,
subsample=0.6))])
cv score: [0.62787356 0.70114943 0.6637931 0.66537468 0.44056848]
----------------------------------------
Trial 3285
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68678161 0.66522989 0.63649425 0.5620155 0.45994832]
----------------------------------------
Trial 3286
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=154, random_state=100))])
cv score: [0.59913793 0.70186782 0.66954023 0.70542636 0.41666667]
----------------------------------------
Trial 3287
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.67097701 0.61781609 0.63565891 0.40956072]
----------------------------------------
Trial 3288
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=112, random_state=100,
subsample=0.95))])
cv score: [0.56465517 0.73706897 0.61063218 0.60981912 0.39534884]
----------------------------------------
Trial 3289
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='log2',
n_estimators=195, random_state=100,
subsample=0.7))])
cv score: [0.52011494 0.60201149 0.5545977 0.69638243 0.4496124 ]
----------------------------------------
Trial 3290
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.70402299 0.57327586 0.56718346 0.47932817]
----------------------------------------
Trial 3291
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=150,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.81034483 0.61063218 0.66091954 0.61692506 0.44767442]
----------------------------------------
Trial 3292
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.72413793 0.58764368 0.57105943 0.44056848]
----------------------------------------
Trial 3293
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=151,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67672414 0.6091954 0.66666667 0.62015504 0.374677 ]
----------------------------------------
Trial 3294
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=103,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7341954 0.64439655 0.6875 0.60142119 0.42571059]
----------------------------------------
Trial 3295
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=92,
random_state=100,
subsample=0.85))])
cv score: [0.63649425 0.67241379 0.57183908 0.73126615 0.4496124 ]
----------------------------------------
Trial 3296
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=76, random_state=100))])
cv score: [0.69683908 0.66954023 0.66666667 0.53100775 0.37596899]
----------------------------------------
Trial 3297
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=191,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75287356 0.64942529 0.69109195 0.624677 0.44573643]
----------------------------------------
Trial 3298
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=130,
random_state=100))])
cv score: [0.77155172 0.63218391 0.66163793 0.62919897 0.38888889]
----------------------------------------
Trial 3299
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=164, random_state=100))])
cv score: [0.70977011 0.63362069 0.65086207 0.59173127 0.41085271]
----------------------------------------
Trial 3300
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=154, random_state=100,
subsample=0.7))])
cv score: [0.62068966 0.66954023 0.4683908 0.69767442 0.49483204]
----------------------------------------
Trial 3301
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=149,
random_state=100))])
cv score: [0.58477011 0.67241379 0.64367816 0.68346253 0.42894057]
----------------------------------------
Trial 3302
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=62,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.63649425 0.71695402 0.65762274 0.41472868]
----------------------------------------
Trial 3303
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=183, random_state=100,
subsample=0.7))])
cv score: [0.58764368 0.5933908 0.49856322 0.71576227 0.5620155 ]
----------------------------------------
Trial 3304
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.69683908 0.56321839 0.62919897 0.40956072]
----------------------------------------
Trial 3305
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=141, random_state=100))])
cv score: [0.55387931 0.68390805 0.59123563 0.62080103 0.44186047]
----------------------------------------
Trial 3306
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=60,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78663793 0.5941092 0.7033046 0.59883721 0.40503876]
----------------------------------------
Trial 3307
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=149,
random_state=100))])
cv score: [0.77155172 0.63936782 0.68103448 0.63953488 0.39922481]
----------------------------------------
Trial 3308
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=100,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73563218 0.69971264 0.7183908 0.65310078 0.42635659]
----------------------------------------
Trial 3309
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=60, random_state=100,
subsample=0.8))])
cv score: [0.52586207 0.64367816 0.5316092 0.65503876 0.48062016]
----------------------------------------
Trial 3310
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='log2',
n_estimators=50, random_state=100,
subsample=0.9))])
cv score: [0.61781609 0.6637931 0.46695402 0.67829457 0.47416021]
----------------------------------------
Trial 3311
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=35, random_state=100))])
cv score: [0.58979885 0.67744253 0.61422414 0.69832041 0.46059432]
----------------------------------------
Trial 3312
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=159, random_state=100))])
cv score: [0.59913793 0.70905172 0.66954023 0.70671835 0.42377261]
----------------------------------------
Trial 3313
----------------------------------------
Parameters {'xgb__n_estimators': 69, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=69,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.6637931 0.45977011 0.5374677 0.46382429]
----------------------------------------
Trial 3314
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=132,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.68247126 0.55603448 0.53100775 0.45219638]
----------------------------------------
Trial 3315
----------------------------------------
Parameters {'xgb__n_estimators': 104, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=104,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68534483 0.66666667 0.59051724 0.5620155 0.42894057]
----------------------------------------
Trial 3316
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.70258621 0.6408046 0.62273902 0.43281654]
----------------------------------------
Trial 3317
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.60057471 0.61494253 0.59043928 0.44315245]
----------------------------------------
Trial 3318
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.72557471 0.54885057 0.5245478 0.43410853]
----------------------------------------
Trial 3319
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=172,
random_state=100))])
cv score: [0.56321839 0.61063218 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 3320
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.95))])
cv score: [0.61063218 0.65804598 0.57471264 0.69121447 0.45736434]
----------------------------------------
Trial 3321
----------------------------------------
Parameters {'gb__n_estimators': 183, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=183, random_state=100,
subsample=0.6))])
cv score: [0.41235632 0.52586207 0.62356322 0.63824289 0.40956072]
----------------------------------------
Trial 3322
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=15,
random_state=100))])
cv score: [0.61997126 0.54741379 0.4841954 0.63178295 0.42635659]
----------------------------------------
Trial 3323
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=37, random_state=100))])
cv score: [0.64942529 0.62787356 0.43247126 0.65503876 0.42377261]
----------------------------------------
Trial 3324
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='log2',
n_estimators=145, random_state=100,
subsample=0.65))])
cv score: [0.62356322 0.68534483 0.62212644 0.64470284 0.48449612]
----------------------------------------
Trial 3325
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=188, random_state=100,
subsample=0.85))])
cv score: [0.60632184 0.71551724 0.55172414 0.6873385 0.41860465]
----------------------------------------
Trial 3326
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=153, random_state=100))])
cv score: [0.63505747 0.66091954 0.57758621 0.66666667 0.38888889]
----------------------------------------
Trial 3327
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=97, random_state=100))])
cv score: [0.59267241 0.70833333 0.64295977 0.65633075 0.44638243]
----------------------------------------
Trial 3328
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=148, random_state=100,
subsample=0.6))])
cv score: [0.72701149 0.64942529 0.69109195 0.64857881 0.4121447 ]
----------------------------------------
Trial 3329
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
max_features='log2',
n_estimators=21, random_state=100,
subsample=0.6))])
cv score: [0.82758621 0.61278736 0.71767241 0.61757106 0.36692506]
----------------------------------------
Trial 3330
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.65))])
cv score: [0.64511494 0.66810345 0.63649425 0.60852713 0.47416021]
----------------------------------------
Trial 3331
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=78, random_state=100,
subsample=0.65))])
cv score: [0.70402299 0.66738506 0.49568966 0.64599483 0.44315245]
----------------------------------------
Trial 3332
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=82,
random_state=100))])
cv score: [0.71982759 0.65804598 0.66091954 0.65762274 0.38501292]
----------------------------------------
Trial 3333
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=114, random_state=100))])
cv score: [0.60560345 0.71767241 0.63362069 0.66020672 0.43023256]
----------------------------------------
Trial 3334
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
n_estimators=81, random_state=100,
subsample=0.65))])
cv score: [0.57471264 0.68821839 0.61350575 0.67958656 0.43023256]
----------------------------------------
Trial 3335
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='sqrt',
n_estimators=168, random_state=100,
subsample=0.95))])
cv score: [0.62787356 0.7183908 0.47413793 0.65245478 0.44573643]
----------------------------------------
Trial 3336
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=115, random_state=100,
subsample=0.6))])
cv score: [0.60344828 0.62068966 0.60201149 0.65891473 0.39664083]
----------------------------------------
Trial 3337
----------------------------------------
Parameters {'gb__n_estimators': 123, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
n_estimators=123,
random_state=100))])
cv score: [0.52442529 0.59554598 0.62931034 0.58010336 0.45090439]
----------------------------------------
Trial 3338
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=110, random_state=100))])
cv score: [0.61206897 0.67385057 0.60201149 0.64341085 0.43023256]
----------------------------------------
Trial 3339
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=130,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70545977 0.65373563 0.68390805 0.625323 0.46770026]
----------------------------------------
Trial 3340
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=91,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.6637931 0.68103448 0.62919897 0.40180879]
----------------------------------------
Trial 3341
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7112069 0.62356322 0.67025862 0.63178295 0.42958656]
----------------------------------------
Trial 3342
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=74, random_state=100))])
cv score: [0.72270115 0.60632184 0.57758621 0.57881137 0.43023256]
----------------------------------------
Trial 3343
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=18,
random_state=100))])
cv score: [0.59985632 0.64224138 0.60201149 0.55103359 0.40439276]
----------------------------------------
Trial 3344
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.69252874 0.69827586 0.6369509 0.4121447 ]
----------------------------------------
Trial 3345
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=172, random_state=100))])
cv score: [0.77298851 0.63793103 0.5704023 0.62144703 0.39728682]
----------------------------------------
Trial 3346
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=34,
random_state=100))])
cv score: [0.79238506 0.63002874 0.67241379 0.66860465 0.43023256]
----------------------------------------
Trial 3347
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=22,
random_state=100))])
cv score: [0.63936782 0.60488506 0.56178161 0.51744186 0.40116279]
----------------------------------------
Trial 3348
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=151,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68965517 0.71695402 0.65229885 0.63824289 0.41472868]
----------------------------------------
Trial 3349
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=38, random_state=100,
subsample=0.95))])
cv score: [0.59482759 0.74137931 0.51724138 0.61498708 0.3378553 ]
----------------------------------------
Trial 3350
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='sqrt',
n_estimators=32, random_state=100,
subsample=0.95))])
cv score: [0.58764368 0.70833333 0.59482759 0.6627907 0.41602067]
----------------------------------------
Trial 3351
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=185,
random_state=100))])
cv score: [0.54597701 0.65229885 0.59051724 0.74160207 0.40116279]
----------------------------------------
Trial 3352
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.66091954 0.65948276 0.63436693 0.43540052]
----------------------------------------
Trial 3353
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=62,
random_state=100))])
cv score: [0.57471264 0.69109195 0.57183908 0.65374677 0.38372093]
----------------------------------------
Trial 3354
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=104,
random_state=100))])
cv score: [0.61278736 0.67887931 0.63577586 0.70671835 0.38824289]
----------------------------------------
Trial 3355
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=38,
random_state=100))])
cv score: [0.5316092 0.56034483 0.56465517 0.69509044 0.39664083]
----------------------------------------
Trial 3356
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='sqrt',
n_estimators=91, random_state=100,
subsample=0.95))])
cv score: [0.6091954 0.68390805 0.5316092 0.59431525 0.47286822]
----------------------------------------
Trial 3357
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=198,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 3358
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=175, random_state=100))])
cv score: [0.63649425 0.65086207 0.5862069 0.67958656 0.37596899]
----------------------------------------
Trial 3359
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='log2',
n_estimators=108,
random_state=100))])
cv score: [0.75287356 0.64798851 0.75431034 0.6375969 0.41085271]
----------------------------------------
Trial 3360
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=56, random_state=100))])
cv score: [0.54238506 0.63721264 0.57183908 0.61175711 0.45671835]
----------------------------------------
Trial 3361
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=28,
random_state=100))])
cv score: [0.60775862 0.69396552 0.54310345 0.75193798 0.42764858]
----------------------------------------
Trial 3362
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=152,
random_state=100))])
cv score: [0.77155172 0.63362069 0.68175287 0.65762274 0.41343669]
----------------------------------------
Trial 3363
----------------------------------------
Parameters {'gb__n_estimators': 37, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=5,
max_features='log2',
n_estimators=37, random_state=100,
subsample=0.7))])
cv score: [0.71408046 0.67385057 0.54454023 0.59819121 0.51033592]
----------------------------------------
Trial 3364
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=72, random_state=100,
subsample=0.7))])
cv score: [0.64798851 0.70689655 0.4454023 0.68087855 0.46770026]
----------------------------------------
Trial 3365
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=94, random_state=100))])
cv score: [0.73563218 0.63074713 0.66666667 0.58139535 0.4005168 ]
----------------------------------------
Trial 3366
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=175,
random_state=100))])
cv score: [0.59051724 0.67456897 0.61997126 0.70284238 0.39728682]
----------------------------------------
Trial 3367
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=33,
random_state=100, subsample=0.9))])
cv score: [0.54885057 0.70977011 0.49137931 0.68087855 0.39147287]
----------------------------------------
Trial 3368
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.55244253 0.68175287 0.5862069 0.62080103 0.44186047]
----------------------------------------
Trial 3369
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=165, random_state=100))])
cv score: [0.63793103 0.66091954 0.60775862 0.61627907 0.45736434]
----------------------------------------
Trial 3370
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=108, random_state=100,
subsample=0.7))])
cv score: [0.55890805 0.63218391 0.55028736 0.70155039 0.45219638]
----------------------------------------
Trial 3371
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=174,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.66522989 0.66666667 0.57622739 0.45478036]
----------------------------------------
Trial 3372
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.9))])
cv score: [0.56752874 0.68247126 0.55747126 0.65503876 0.43927649]
----------------------------------------
Trial 3373
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=145,
random_state=100))])
cv score: [0.64008621 0.59770115 0.56178161 0.51744186 0.41860465]
----------------------------------------
Trial 3374
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=164,
random_state=100))])
cv score: [0.77298851 0.62643678 0.68893678 0.65503876 0.40568475]
----------------------------------------
Trial 3375
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5545977 0.68103448 0.69109195 0.60465116 0.41860465]
----------------------------------------
Trial 3376
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=95, random_state=100))])
cv score: [0.56321839 0.69324713 0.60272989 0.60142119 0.44573643]
----------------------------------------
Trial 3377
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=172,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76436782 0.67097701 0.68247126 0.60723514 0.44767442]
----------------------------------------
Trial 3378
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.64655172 0.66091954 0.63307494 0.40697674]
----------------------------------------
Trial 3379
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, max_features='log2',
n_estimators=184, random_state=100,
subsample=0.7))])
cv score: [0.66522989 0.68390805 0.48850575 0.51550388 0.50387597]
----------------------------------------
Trial 3380
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=180,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77155172 0.63793103 0.6408046 0.63630491 0.44056848]
----------------------------------------
Trial 3381
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=132, random_state=100))])
cv score: [0.64798851 0.66810345 0.56465517 0.67054264 0.3630491 ]
----------------------------------------
Trial 3382
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=104, random_state=100))])
cv score: [0.54885057 0.67456897 0.61135057 0.58527132 0.40826873]
----------------------------------------
Trial 3383
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=105,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.69683908 0.56034483 0.54392765 0.45090439]
----------------------------------------
Trial 3384
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.69396552 0.60632184 0.60981912 0.40310078]
----------------------------------------
Trial 3385
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.65))])
cv score: [0.44971264 0.63074713 0.56321839 0.6744186 0.43152455]
----------------------------------------
Trial 3386
----------------------------------------
Parameters {'gb__n_estimators': 41, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=41, random_state=100,
subsample=0.85))])
cv score: [0.74497126 0.68247126 0.46623563 0.625323 0.39922481]
----------------------------------------
Trial 3387
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=10,
random_state=100))])
cv score: [0.70761494 0.57614943 0.62643678 0.68863049 0.37209302]
----------------------------------------
Trial 3388
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, n_estimators=94,
random_state=100,
subsample=0.75))])
cv score: [0.64655172 0.59482759 0.62068966 0.48901809 0.52713178]
----------------------------------------
Trial 3389
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
n_estimators=138, random_state=100,
subsample=0.85))])
cv score: [0.54597701 0.7183908 0.61494253 0.73385013 0.43023256]
----------------------------------------
Trial 3390
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.625 0.69971264 0.65086207 0.63565891 0.42377261]
----------------------------------------
Trial 3391
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=151, random_state=100,
subsample=0.65))])
cv score: [0.6637931 0.6637931 0.60775862 0.64470284 0.40568475]
----------------------------------------
Trial 3392
----------------------------------------
Parameters {'gb__n_estimators': 57, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=57, random_state=100,
subsample=0.7))])
cv score: [0.56465517 0.41810345 0.59913793 0.62661499 0.55555556]
----------------------------------------
Trial 3393
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=185,
random_state=100))])
cv score: [0.75862069 0.64942529 0.68821839 0.64470284 0.44056848]
----------------------------------------
Trial 3394
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.65517241 0.63793103 0.62790698 0.38888889]
----------------------------------------
Trial 3395
----------------------------------------
Parameters {'rf__n_estimators': 121, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=121,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 3396
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=54,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65373563 0.65229885 0.68103448 0.625323 0.42700258]
----------------------------------------
Trial 3397
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=83,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.65229885 0.43247126 0.50129199 0.35529716]
----------------------------------------
Trial 3398
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=89,
random_state=100))])
cv score: [0.78735632 0.61781609 0.66810345 0.625323 0.40568475]
----------------------------------------
Trial 3399
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=134,
random_state=100))])
cv score: [0.68031609 0.57399425 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 3400
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=162, random_state=100))])
cv score: [0.56465517 0.67816092 0.60272989 0.60465116 0.44315245]
----------------------------------------
Trial 3401
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='sqrt',
n_estimators=153,
random_state=100))])
cv score: [0.5545977 0.70402299 0.44109195 0.67829457 0.45219638]
----------------------------------------
Trial 3402
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=145, random_state=100,
subsample=0.8))])
cv score: [0.65804598 0.64367816 0.43965517 0.56718346 0.42894057]
----------------------------------------
Trial 3403
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
n_estimators=110, random_state=100,
subsample=0.85))])
cv score: [0.53591954 0.67385057 0.55316092 0.5878553 0.40568475]
----------------------------------------
Trial 3404
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=99,
random_state=100))])
cv score: [0.63362069 0.66954023 0.59626437 0.62790698 0.41602067]
----------------------------------------
Trial 3405
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
n_estimators=137, random_state=100,
subsample=0.75))])
cv score: [0.63074713 0.68534483 0.60344828 0.66408269 0.43540052]
----------------------------------------
Trial 3406
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=10,
max_features='sqrt',
n_estimators=120, random_state=100,
subsample=0.6))])
cv score: [0.57327586 0.68103448 0.44109195 0.63049096 0.4379845 ]
----------------------------------------
Trial 3407
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=42,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56034483 0.6954023 0.6954023 0.60465116 0.41343669]
----------------------------------------
Trial 3408
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=138, random_state=100,
subsample=0.8))])
cv score: [0.5933908 0.63362069 0.52442529 0.51162791 0.50387597]
----------------------------------------
Trial 3409
----------------------------------------
Parameters {'xgb__n_estimators': 12, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=12,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63362069 0.56609195 0.55747126 0.56072351 0.36434109]
----------------------------------------
Trial 3410
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77155172 0.67744253 0.68965517 0.61692506 0.40245478]
----------------------------------------
Trial 3411
----------------------------------------
Parameters {'xgb__n_estimators': 43, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=43,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.71551724 0.69252874 0.66149871 0.43152455]
----------------------------------------
Trial 3412
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=42,
random_state=100))])
cv score: [0.78017241 0.63649425 0.62715517 0.65245478 0.40568475]
----------------------------------------
Trial 3413
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=168, random_state=100,
subsample=0.7))])
cv score: [0.66235632 0.69252874 0.47413793 0.6627907 0.46899225]
----------------------------------------
Trial 3414
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=30, random_state=100,
subsample=0.7))])
cv score: [0.5158046 0.70689655 0.4295977 0.64728682 0.46382429]
----------------------------------------
Trial 3415
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=95,
random_state=100))])
cv score: [0.63362069 0.66666667 0.60632184 0.62661499 0.39922481]
----------------------------------------
Trial 3416
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=132,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 3417
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=176, random_state=100))])
cv score: [0.63936782 0.66235632 0.60775862 0.61757106 0.45607235]
----------------------------------------
Trial 3418
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65301724 0.58477011 0.61925287 0.53488372 0.36175711]
----------------------------------------
Trial 3419
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='sqrt',
n_estimators=49, random_state=100,
subsample=0.85))])
cv score: [0.61637931 0.72270115 0.51867816 0.67571059 0.45090439]
----------------------------------------
Trial 3420
----------------------------------------
Parameters {'xgb__n_estimators': 26, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=26,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.66235632 0.57614943 0.57622739 0.43927649]
----------------------------------------
Trial 3421
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=115, random_state=100,
subsample=0.8))])
cv score: [0.69971264 0.65517241 0.67097701 0.6369509 0.42894057]
----------------------------------------
Trial 3422
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=118,
random_state=100))])
cv score: [0.55603448 0.66020115 0.60201149 0.74031008 0.41795866]
----------------------------------------
Trial 3423
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='log2',
n_estimators=89, random_state=100,
subsample=0.8))])
cv score: [0.59051724 0.58764368 0.5704023 0.59819121 0.39534884]
----------------------------------------
Trial 3424
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=116, random_state=100))])
cv score: [0.60775862 0.6795977 0.59195402 0.62919897 0.41989664]
----------------------------------------
Trial 3425
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=56, random_state=100,
subsample=0.9))])
cv score: [0.4841954 0.70545977 0.52011494 0.60206718 0.51808786]
----------------------------------------
Trial 3426
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='log2',
n_estimators=189, random_state=100,
subsample=0.9))])
cv score: [0.54022989 0.79310345 0.51293103 0.48966408 0.43023256]
----------------------------------------
Trial 3427
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=174, random_state=100))])
cv score: [0.62931034 0.65229885 0.62068966 0.6744186 0.39534884]
----------------------------------------
Trial 3428
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=134,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64942529 0.69109195 0.62212644 0.6124031 0.39664083]
----------------------------------------
Trial 3429
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=143, random_state=100))])
cv score: [0.71551724 0.64511494 0.67385057 0.59431525 0.39922481]
----------------------------------------
Trial 3430
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=168, random_state=100,
subsample=0.6))])
cv score: [0.62787356 0.43821839 0.52729885 0.78940568 0.50387597]
----------------------------------------
Trial 3431
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
n_estimators=145, random_state=100,
subsample=0.7))])
cv score: [0.73275862 0.67241379 0.65086207 0.625323 0.43152455]
----------------------------------------
Trial 3432
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=97,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76364943 0.58117816 0.68534483 0.62984496 0.43346253]
----------------------------------------
Trial 3433
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=58, random_state=100,
subsample=0.75))])
cv score: [0.57614943 0.69683908 0.4295977 0.62403101 0.39664083]
----------------------------------------
Trial 3434
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57614943 0.6954023 0.61350575 0.61886305 0.41343669]
----------------------------------------
Trial 3435
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=155,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.70977011 0.59913793 0.62661499 0.45994832]
----------------------------------------
Trial 3436
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=110, random_state=100,
subsample=0.6))])
cv score: [0.55172414 0.65373563 0.59051724 0.65633075 0.44702842]
----------------------------------------
Trial 3437
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='log2',
n_estimators=138, random_state=100,
subsample=0.8))])
cv score: [0.55603448 0.39655172 0.63218391 0.56459948 0.51033592]
----------------------------------------
Trial 3438
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=162, random_state=100))])
cv score: [0.60775862 0.69971264 0.66666667 0.69509044 0.44056848]
----------------------------------------
Trial 3439
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=125,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.68965517 0.60344828 0.66537468 0.41085271]
----------------------------------------
Trial 3440
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=75,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56321839 0.68678161 0.69827586 0.51550388 0.33850129]
----------------------------------------
Trial 3441
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
n_estimators=17, random_state=100,
subsample=0.75))])
cv score: [0.53591954 0.62787356 0.59482759 0.63178295 0.3624031 ]
----------------------------------------
Trial 3442
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=118, random_state=100))])
cv score: [0.63793103 0.67816092 0.60775862 0.66408269 0.43152455]
----------------------------------------
Trial 3443
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.68678161 0.5862069 0.5374677 0.44832041]
----------------------------------------
Trial 3444
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=159, random_state=100,
subsample=0.85))])
cv score: [0.56178161 0.74856322 0.62212644 0.7002584 0.38888889]
----------------------------------------
Trial 3445
----------------------------------------
Parameters {'xgb__n_estimators': 95, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=95,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67816092 0.62212644 0.65804598 0.60077519 0.41989664]
----------------------------------------
Trial 3446
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=187, random_state=100))])
cv score: [0.59051724 0.69324713 0.58189655 0.62144703 0.42829457]
----------------------------------------
Trial 3447
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=103, random_state=100))])
cv score: [0.56968391 0.70186782 0.6170977 0.58850129 0.43863049]
----------------------------------------
Trial 3448
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='sqrt',
n_estimators=180, random_state=100,
subsample=0.7))])
cv score: [0.61781609 0.63074713 0.48132184 0.58010336 0.42764858]
----------------------------------------
Trial 3449
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=59, random_state=100,
subsample=0.9))])
cv score: [0.77586207 0.60057471 0.72126437 0.66149871 0.38501292]
----------------------------------------
Trial 3450
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=137, random_state=100,
subsample=0.7))])
cv score: [0.4295977 0.57758621 0.42097701 0.49483204 0.39018088]
----------------------------------------
Trial 3451
----------------------------------------
Parameters {'xgb__n_estimators': 111, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=111,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.69971264 0.61206897 0.60335917 0.40439276]
----------------------------------------
Trial 3452
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=31,
random_state=100, subsample=0.8))])
cv score: [0.54166667 0.76005747 0.57902299 0.65891473 0.42764858]
----------------------------------------
Trial 3453
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51149425 0.72270115 0.54741379 0.54780362 0.38113695]
----------------------------------------
Trial 3454
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=43, random_state=100))])
cv score: [0.57112069 0.72054598 0.65445402 0.68281654 0.43475452]
----------------------------------------
Trial 3455
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=143,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74712644 0.69971264 0.71982759 0.61950904 0.39534884]
----------------------------------------
Trial 3456
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=17, random_state=100,
subsample=0.95))])
cv score: [0.65804598 0.66954023 0.69827586 0.69509044 0.4870801 ]
----------------------------------------
Trial 3457
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=147,
random_state=100))])
cv score: [0.77011494 0.63505747 0.68031609 0.65503876 0.40956072]
----------------------------------------
Trial 3458
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=106, random_state=100,
subsample=0.95))])
cv score: [0.61350575 0.74137931 0.55603448 0.64082687 0.4502584 ]
----------------------------------------
Trial 3459
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=114,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7262931 0.59123563 0.67600575 0.62144703 0.43927649]
----------------------------------------
Trial 3460
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76005747 0.61278736 0.70545977 0.62015504 0.46705426]
----------------------------------------
Trial 3461
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=164,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 3462
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=26, random_state=100,
subsample=0.9))])
cv score: [0.6408046 0.6795977 0.58477011 0.69896641 0.3875969 ]
----------------------------------------
Trial 3463
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=126,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.74425287 0.63074713 0.61111111 0.40180879]
----------------------------------------
Trial 3464
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=161, random_state=100))])
cv score: [0.72557471 0.63505747 0.6637931 0.64082687 0.41602067]
----------------------------------------
Trial 3465
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=141,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.72844828 0.68678161 0.52196382 0.38501292]
----------------------------------------
Trial 3466
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=54,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74712644 0.56178161 0.67744253 0.65245478 0.38372093]
----------------------------------------
Trial 3467
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=97,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77442529 0.5704023 0.69324713 0.54586563 0.43152455]
----------------------------------------
Trial 3468
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75359195 0.62140805 0.6716954 0.55749354 0.3998708 ]
----------------------------------------
Trial 3469
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70545977 0.64942529 0.64942529 0.66925065 0.41085271]
----------------------------------------
Trial 3470
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.65229885 0.56034483 0.55684755 0.38501292]
----------------------------------------
Trial 3471
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
n_estimators=52, random_state=100,
subsample=0.65))])
cv score: [0.5316092 0.48994253 0.37068966 0.54909561 0.55555556]
----------------------------------------
Trial 3472
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=163,
random_state=100))])
cv score: [0.59195402 0.6795977 0.57758621 0.69121447 0.42248062]
----------------------------------------
Trial 3473
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=75, random_state=100,
subsample=0.85))])
cv score: [0.48994253 0.65086207 0.48994253 0.71705426 0.50775194]
----------------------------------------
Trial 3474
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=179, random_state=100,
subsample=0.85))])
cv score: [0.69252874 0.69252874 0.69827586 0.63307494 0.44315245]
----------------------------------------
Trial 3475
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=91, random_state=100,
subsample=0.95))])
cv score: [0.56896552 0.70545977 0.57614943 0.67312661 0.43410853]
----------------------------------------
Trial 3476
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.65804598 0.57471264 0.56847545 0.41085271]
----------------------------------------
Trial 3477
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=132,
random_state=100))])
cv score: [0.7341954 0.64798851 0.58333333 0.63178295 0.41860465]
----------------------------------------
Trial 3478
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=89, random_state=100,
subsample=0.9))])
cv score: [0.64224138 0.67097701 0.45114943 0.60594315 0.48062016]
----------------------------------------
Trial 3479
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.69827586 0.65804598 0.58010336 0.42248062]
----------------------------------------
Trial 3480
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=61,
random_state=100))])
cv score: [0.52729885 0.73347701 0.63074713 0.69896641 0.46447028]
----------------------------------------
Trial 3481
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=32,
random_state=100))])
cv score: [0.62356322 0.65373563 0.6408046 0.65116279 0.33656331]
----------------------------------------
Trial 3482
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=113,
random_state=100))])
cv score: [0.68031609 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 3483
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=167, random_state=100))])
cv score: [0.72844828 0.65373563 0.64942529 0.66925065 0.43152455]
----------------------------------------
Trial 3484
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=108,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.76724138 0.56321839 0.61498708 0.45348837]
----------------------------------------
Trial 3485
----------------------------------------
Parameters {'xgb__n_estimators': 167, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=167,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5704023 0.68247126 0.63362069 0.64211886 0.42118863]
----------------------------------------
Trial 3486
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='log2',
n_estimators=101, random_state=100,
subsample=0.8))])
cv score: [0.63505747 0.70258621 0.5316092 0.62790698 0.49741602]
----------------------------------------
Trial 3487
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=100,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75215517 0.61206897 0.67456897 0.62144703 0.42958656]
----------------------------------------
Trial 3488
----------------------------------------
Parameters {'gb__n_estimators': 146, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=146, random_state=100,
subsample=0.7))])
cv score: [0.61781609 0.69971264 0.51149425 0.59689922 0.50645995]
----------------------------------------
Trial 3489
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=120, random_state=100))])
cv score: [0.59913793 0.70186782 0.64942529 0.69121447 0.42894057]
----------------------------------------
Trial 3490
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65229885 0.65373563 0.6408046 0.5749354 0.42635659]
----------------------------------------
Trial 3491
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=156,
random_state=100))])
cv score: [0.5933908 0.68390805 0.5387931 0.6744186 0.44056848]
----------------------------------------
Trial 3492
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=80,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.67241379 0.61781609 0.66020672 0.40697674]
----------------------------------------
Trial 3493
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=139, random_state=100))])
cv score: [0.6954023 0.66810345 0.64224138 0.68217054 0.43669251]
----------------------------------------
Trial 3494
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=84, random_state=100,
subsample=0.9))])
cv score: [0.56034483 0.72270115 0.55747126 0.73643411 0.4250646 ]
----------------------------------------
Trial 3495
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=80,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.58189655 0.58333333 0.66537468 0.41860465]
----------------------------------------
Trial 3496
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=161,
random_state=100))])
cv score: [0.61350575 0.66954023 0.60991379 0.72093023 0.41085271]
----------------------------------------
Trial 3497
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=106,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71982759 0.7183908 0.68821839 0.60465116 0.42958656]
----------------------------------------
Trial 3498
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=105,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75862069 0.56609195 0.58979885 0.54715762 0.4121447 ]
----------------------------------------
Trial 3499
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=69,
random_state=100))])
cv score: [0.64367816 0.62212644 0.63362069 0.62144703 0.39793282]
----------------------------------------
Trial 3500
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=35,
random_state=100))])
cv score: [0.68318966 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 3501
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=83,
random_state=100))])
cv score: [0.61422414 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 3502
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7887931 0.58045977 0.7341954 0.61950904 0.50516796]
----------------------------------------
Trial 3503
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
random_state=100, subsample=0.9))])
cv score: [0.62212644 0.59913793 0.54597701 0.6369509 0.50775194]
----------------------------------------
Trial 3504
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.70833333 0.60344828 0.58139535 0.42894057]
----------------------------------------
Trial 3505
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=171, random_state=100))])
cv score: [0.58477011 0.73347701 0.63433908 0.67829457 0.44186047]
----------------------------------------
Trial 3506
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=194, random_state=100))])
cv score: [0.64511494 0.66522989 0.59626437 0.62015504 0.45607235]
----------------------------------------
Trial 3507
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=90, random_state=100,
subsample=0.7))])
cv score: [0.7183908 0.65804598 0.47701149 0.62273902 0.42377261]
----------------------------------------
Trial 3508
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.66235632 0.61494253 0.54521964 0.44186047]
----------------------------------------
Trial 3509
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
n_estimators=173, random_state=100,
subsample=0.7))])
cv score: [0.57614943 0.62212644 0.64224138 0.60852713 0.53229974]
----------------------------------------
Trial 3510
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=144,
random_state=100))])
cv score: [0.61637931 0.59770115 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 3511
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
n_estimators=81, random_state=100,
subsample=0.7))])
cv score: [0.52586207 0.39367816 0.56752874 0.63565891 0.53617571]
----------------------------------------
Trial 3512
----------------------------------------
Parameters {'rf__n_estimators': 143, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=143,
random_state=100))])
cv score: [0.58908046 0.68462644 0.59626437 0.68604651 0.4250646 ]
----------------------------------------
Trial 3513
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=77,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62068966 0.70114943 0.66810345 0.5994832 0.4121447 ]
----------------------------------------
Trial 3514
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=12, random_state=100))])
cv score: [0.50933908 0.62643678 0.61853448 0.56782946 0.56330749]
----------------------------------------
Trial 3515
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.68821839 0.59482759 0.58010336 0.43669251]
----------------------------------------
Trial 3516
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=81, random_state=100,
subsample=0.6))])
cv score: [0.66954023 0.70689655 0.58908046 0.63436693 0.44702842]
----------------------------------------
Trial 3517
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=195, random_state=100,
subsample=0.7))])
cv score: [0.61637931 0.66522989 0.52873563 0.65891473 0.46382429]
----------------------------------------
Trial 3518
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=133,
random_state=100))])
cv score: [0.60057471 0.67025862 0.63793103 0.71705426 0.40310078]
----------------------------------------
Trial 3519
----------------------------------------
Parameters {'gb__n_estimators': 86, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=86, random_state=100,
subsample=0.65))])
cv score: [0.5316092 0.64367816 0.51436782 0.67958656 0.4379845 ]
----------------------------------------
Trial 3520
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63362069 0.70402299 0.5933908 0.53617571 0.42118863]
----------------------------------------
Trial 3521
----------------------------------------
Parameters {'xgb__n_estimators': 134, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=134,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.6795977 0.63362069 0.63049096 0.46899225]
----------------------------------------
Trial 3522
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=147, random_state=100,
subsample=0.95))])
cv score: [0.63074713 0.71695402 0.55890805 0.63824289 0.41343669]
----------------------------------------
Trial 3523
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=118,
random_state=100))])
cv score: [0.70689655 0.64798851 0.65948276 0.65374677 0.41472868]
----------------------------------------
Trial 3524
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='log2',
n_estimators=77, random_state=100,
subsample=0.75))])
cv score: [0.58189655 0.67816092 0.49568966 0.64211886 0.41602067]
----------------------------------------
Trial 3525
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=78,
random_state=100))])
cv score: [0.56752874 0.67241379 0.62931034 0.72739018 0.36046512]
----------------------------------------
Trial 3526
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62643678 0.68678161 0.62356322 0.60852713 0.38501292]
----------------------------------------
Trial 3527
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
n_estimators=160, random_state=100,
subsample=0.8))])
cv score: [0.54022989 0.72270115 0.56034483 0.61369509 0.47416021]
----------------------------------------
Trial 3528
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=172, random_state=100,
subsample=0.9))])
cv score: [0.5862069 0.63362069 0.44971264 0.5620155 0.50645995]
----------------------------------------
Trial 3529
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=91,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.6795977 0.66235632 0.54521964 0.44056848]
----------------------------------------
Trial 3530
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=131,
random_state=100))])
cv score: [0.60344828 0.67025862 0.63362069 0.71705426 0.4005168 ]
----------------------------------------
Trial 3531
----------------------------------------
Parameters {'rf__n_estimators': 154, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=154, random_state=100))])
cv score: [0.5545977 0.66235632 0.61422414 0.62273902 0.43152455]
----------------------------------------
Trial 3532
----------------------------------------
Parameters {'xgb__n_estimators': 36, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=36,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64367816 0.6954023 0.57614943 0.63824289 0.36950904]
----------------------------------------
Trial 3533
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=170, random_state=100,
subsample=0.85))])
cv score: [0.60201149 0.6795977 0.51867816 0.71705426 0.44573643]
----------------------------------------
Trial 3534
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=172,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.66091954 0.63649425 0.6124031 0.45607235]
----------------------------------------
Trial 3535
----------------------------------------
Parameters {'gb__n_estimators': 85, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=85, random_state=100,
subsample=0.75))])
cv score: [0.74712644 0.6408046 0.64942529 0.61886305 0.41602067]
----------------------------------------
Trial 3536
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5933908 0.72413793 0.63793103 0.58397933 0.42894057]
----------------------------------------
Trial 3537
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=109,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79956897 0.57471264 0.60416667 0.55167959 0.39341085]
----------------------------------------
Trial 3538
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='sqrt',
n_estimators=106, random_state=100,
subsample=0.75))])
cv score: [0.5933908 0.66954023 0.56034483 0.56976744 0.39405685]
----------------------------------------
Trial 3539
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.71264368 0.54310345 0.58397933 0.43540052]
----------------------------------------
Trial 3540
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76005747 0.59698276 0.67672414 0.65310078 0.45865633]
----------------------------------------
Trial 3541
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=104, random_state=100,
subsample=0.7))])
cv score: [0.6637931 0.62356322 0.54597701 0.63178295 0.50258398]
----------------------------------------
Trial 3542
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=109,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.69396552 0.69396552 0.64082687 0.41989664]
----------------------------------------
Trial 3543
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=90, random_state=100))])
cv score: [0.73132184 0.60775862 0.56321839 0.57105943 0.42894057]
----------------------------------------
Trial 3544
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='log2',
n_estimators=49, random_state=100,
subsample=0.8))])
cv score: [0.69827586 0.53735632 0.5545977 0.66408269 0.44186047]
----------------------------------------
Trial 3545
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58764368 0.7183908 0.63649425 0.62790698 0.39664083]
----------------------------------------
Trial 3546
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=65,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56609195 0.64224138 0.61925287 0.63307494 0.42248062]
----------------------------------------
Trial 3547
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=108, random_state=100,
subsample=0.6))])
cv score: [0.6795977 0.62931034 0.52155172 0.66925065 0.41602067]
----------------------------------------
Trial 3548
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=171, random_state=100))])
cv score: [0.71551724 0.6408046 0.65229885 0.64857881 0.41343669]
----------------------------------------
Trial 3549
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, n_estimators=141,
random_state=100, subsample=0.8))])
cv score: [0.58333333 0.75287356 0.50718391 0.68217054 0.43152455]
----------------------------------------
Trial 3550
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=150, random_state=100))])
cv score: [0.76293103 0.62643678 0.67816092 0.63307494 0.40826873]
----------------------------------------
Trial 3551
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=28,
random_state=100, subsample=0.8))])
cv score: [0.66810345 0.54310345 0.80890805 0.56459948 0.50129199]
----------------------------------------
Trial 3552
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.61781609 0.66235632 0.60077519 0.39018088]
----------------------------------------
Trial 3553
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=27, random_state=100,
subsample=0.75))])
cv score: [0.65517241 0.63936782 0.56178161 0.64211886 0.46382429]
----------------------------------------
Trial 3554
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=185, random_state=100))])
cv score: [0.73275862 0.62787356 0.65086207 0.60335917 0.42118863]
----------------------------------------
Trial 3555
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=62,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.65948276 0.65086207 0.62790698 0.44896641]
----------------------------------------
Trial 3556
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72270115 0.6795977 0.71982759 0.61563307 0.41860465]
----------------------------------------
Trial 3557
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=115,
random_state=100))])
cv score: [0.61422414 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 3558
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, n_estimators=162,
random_state=100,
subsample=0.75))])
cv score: [0.68103448 0.68103448 0.64511494 0.63436693 0.45607235]
----------------------------------------
Trial 3559
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='log2',
n_estimators=47, random_state=100,
subsample=0.8))])
cv score: [0.7183908 0.57902299 0.68103448 0.62919897 0.45219638]
----------------------------------------
Trial 3560
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=169,
random_state=100))])
cv score: [0.64511494 0.67385057 0.57327586 0.63953488 0.43152455]
----------------------------------------
Trial 3561
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75933908 0.59051724 0.59841954 0.56072351 0.42958656]
----------------------------------------
Trial 3562
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
n_estimators=141, random_state=100,
subsample=0.65))])
cv score: [0.7112069 0.65086207 0.61206897 0.6498708 0.40310078]
----------------------------------------
Trial 3563
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=168, random_state=100))])
cv score: [0.56824713 0.67097701 0.60201149 0.60465116 0.43410853]
----------------------------------------
Trial 3564
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
n_estimators=190, random_state=100,
subsample=0.6))])
cv score: [0.60488506 0.63362069 0.55028736 0.6744186 0.47674419]
----------------------------------------
Trial 3565
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64367816 0.7341954 0.58477011 0.59043928 0.46899225]
----------------------------------------
Trial 3566
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='log2',
n_estimators=61, random_state=100,
subsample=0.75))])
cv score: [0.7816092 0.65804598 0.58908046 0.61111111 0.42635659]
----------------------------------------
Trial 3567
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=159,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.6637931 0.55747126 0.59302326 0.41343669]
----------------------------------------
Trial 3568
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=179,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53591954 0.72701149 0.5933908 0.64211886 0.37209302]
----------------------------------------
Trial 3569
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7658046 0.56681034 0.74568966 0.58591731 0.43346253]
----------------------------------------
Trial 3570
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=76,
random_state=100))])
cv score: [0.59195402 0.64727011 0.62859195 0.67958656 0.37726098]
----------------------------------------
Trial 3571
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=69, random_state=100))])
cv score: [0.56968391 0.70186782 0.64583333 0.68346253 0.42894057]
----------------------------------------
Trial 3572
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=30, random_state=100))])
cv score: [0.52945402 0.67241379 0.52873563 0.56912145 0.47609819]
----------------------------------------
Trial 3573
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=58, random_state=100,
subsample=0.85))])
cv score: [0.65086207 0.65086207 0.46408046 0.63178295 0.38888889]
----------------------------------------
Trial 3574
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=142, random_state=100))])
cv score: [0.59913793 0.71048851 0.66810345 0.69509044 0.41731266]
----------------------------------------
Trial 3575
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=184,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 3576
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=128, random_state=100))])
cv score: [0.55603448 0.66954023 0.61350575 0.60981912 0.39534884]
----------------------------------------
Trial 3577
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.64367816 0.6637931 0.58914729 0.41085271]
----------------------------------------
Trial 3578
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.64942529 0.61206897 0.63049096 0.40180879]
----------------------------------------
Trial 3579
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=168,
random_state=100))])
cv score: [0.65948276 0.65014368 0.61781609 0.62919897 0.4005168 ]
----------------------------------------
Trial 3580
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=63,
random_state=100))])
cv score: [0.61350575 0.59985632 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 3581
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=199, random_state=100))])
cv score: [0.72126437 0.63936782 0.63505747 0.60594315 0.4250646 ]
----------------------------------------
Trial 3582
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56465517 0.63218391 0.57327586 0.58139535 0.47028424]
----------------------------------------
Trial 3583
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='sqrt',
n_estimators=94,
random_state=100))])
cv score: [0.64224138 0.69971264 0.59770115 0.6627907 0.46511628]
----------------------------------------
Trial 3584
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=153, random_state=100))])
cv score: [0.63218391 0.6408046 0.62212644 0.59173127 0.39793282]
----------------------------------------
Trial 3585
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72916667 0.6012931 0.67025862 0.55749354 0.39534884]
----------------------------------------
Trial 3586
----------------------------------------
Parameters {'rf__n_estimators': 147, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=147, random_state=100))])
cv score: [0.66666667 0.68678161 0.61063218 0.66020672 0.41343669]
----------------------------------------
Trial 3587
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=179,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.64224138 0.64511494 0.59302326 0.41602067]
----------------------------------------
Trial 3588
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=42, random_state=100,
subsample=0.75))])
cv score: [0.51293103 0.59626437 0.55172414 0.63824289 0.44444444]
----------------------------------------
Trial 3589
----------------------------------------
Parameters {'xgb__n_estimators': 155, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=155,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.67241379 0.63936782 0.59819121 0.43023256]
----------------------------------------
Trial 3590
----------------------------------------
Parameters {'xgb__n_estimators': 189, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=189,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.73563218 0.68821839 0.55684755 0.48191214]
----------------------------------------
Trial 3591
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=55,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69971264 0.52442529 0.57902299 0.58397933 0.35658915]
----------------------------------------
Trial 3592
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=111,
random_state=100))])
cv score: [0.70977011 0.64942529 0.67097701 0.65762274 0.41472868]
----------------------------------------
Trial 3593
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.73132184 0.56752874 0.58010336 0.41989664]
----------------------------------------
Trial 3594
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='sqrt',
n_estimators=193, random_state=100,
subsample=0.75))])
cv score: [0.62643678 0.7112069 0.54166667 0.65245478 0.45219638]
----------------------------------------
Trial 3595
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
n_estimators=59, random_state=100,
subsample=0.8))])
cv score: [0.59195402 0.70689655 0.56034483 0.68087855 0.45219638]
----------------------------------------
Trial 3596
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=194,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.71264368 0.60775862 0.62919897 0.41343669]
----------------------------------------
Trial 3597
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.61781609 0.58908046 0.64341085 0.42894057]
----------------------------------------
Trial 3598
----------------------------------------
Parameters {'xgb__n_estimators': 122, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=122,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.63649425 0.7112069 0.64341085 0.42829457]
----------------------------------------
Trial 3599
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.72701149 0.625 0.62403101 0.42635659]
----------------------------------------
Trial 3600
----------------------------------------
Parameters {'rf__n_estimators': 192, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=192,
random_state=100))])
cv score: [0.60201149 0.68103448 0.56609195 0.67054264 0.43023256]
----------------------------------------
Trial 3601
----------------------------------------
Parameters {'rf__n_estimators': 140, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=140, random_state=100))])
cv score: [0.75862069 0.62787356 0.68534483 0.63178295 0.41989664]
----------------------------------------
Trial 3602
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=162, random_state=100,
subsample=0.75))])
cv score: [0.56465517 0.71264368 0.54741379 0.69509044 0.42248062]
----------------------------------------
Trial 3603
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=80,
random_state=100))])
cv score: [0.59913793 0.65732759 0.61853448 0.66925065 0.4005168 ]
----------------------------------------
Trial 3604
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=194,
random_state=100))])
cv score: [0.66235632 0.64655172 0.62356322 0.65503876 0.44832041]
----------------------------------------
Trial 3605
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=193,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.69396552 0.59626437 0.64728682 0.40956072]
----------------------------------------
Trial 3606
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=40,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72916667 0.60488506 0.63721264 0.56330749 0.40891473]
----------------------------------------
Trial 3607
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78017241 0.60488506 0.75431034 0.55361757 0.39728682]
----------------------------------------
Trial 3608
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=51, random_state=100,
subsample=0.75))])
cv score: [0.54310345 0.65804598 0.55747126 0.70930233 0.50129199]
----------------------------------------
Trial 3609
----------------------------------------
Parameters {'rf__n_estimators': 95, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=95,
random_state=100))])
cv score: [0.75431034 0.63649425 0.70833333 0.6498708 0.40310078]
----------------------------------------
Trial 3610
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
random_state=100))])
cv score: [0.64367816 0.64942529 0.63649425 0.56718346 0.40568475]
----------------------------------------
Trial 3611
----------------------------------------
Parameters {'gb__n_estimators': 52, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
n_estimators=52, random_state=100,
subsample=0.65))])
cv score: [0.76149425 0.66666667 0.46982759 0.63242894 0.40826873]
----------------------------------------
Trial 3612
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=137, random_state=100,
subsample=0.75))])
cv score: [0.54022989 0.72413793 0.61206897 0.66925065 0.43023256]
----------------------------------------
Trial 3613
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=34, random_state=100,
subsample=0.7))])
cv score: [0.73275862 0.61063218 0.52011494 0.64211886 0.40826873]
----------------------------------------
Trial 3614
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='log2',
n_estimators=33, random_state=100,
subsample=0.75))])
cv score: [0.59195402 0.70402299 0.5933908 0.65633075 0.43023256]
----------------------------------------
Trial 3615
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=56, random_state=100))])
cv score: [0.71264368 0.65948276 0.65086207 0.61111111 0.36046512]
----------------------------------------
Trial 3616
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.61925287 0.6408046 0.54780362 0.43410853]
----------------------------------------
Trial 3617
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.69396552 0.60201149 0.57751938 0.44315245]
----------------------------------------
Trial 3618
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=83,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.69971264 0.63074713 0.59173127 0.40439276]
----------------------------------------
Trial 3619
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=20,
random_state=100))])
cv score: [0.79597701 0.67528736 0.67097701 0.70090439 0.46382429]
----------------------------------------
Trial 3620
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='log2',
n_estimators=129, random_state=100,
subsample=0.9))])
cv score: [0.62643678 0.69109195 0.54741379 0.59819121 0.49741602]
----------------------------------------
Trial 3621
----------------------------------------
Parameters {'xgb__n_estimators': 99, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=99,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.69971264 0.61063218 0.57881137 0.40180879]
----------------------------------------
Trial 3622
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, n_estimators=175,
random_state=100,
subsample=0.65))])
cv score: [0.5933908 0.68247126 0.57902299 0.66795866 0.41085271]
----------------------------------------
Trial 3623
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=170,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.66522989 0.6566092 0.64082687 0.42894057]
----------------------------------------
Trial 3624
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=56, random_state=100,
subsample=0.7))])
cv score: [0.74137931 0.62787356 0.63074713 0.63178295 0.40180879]
----------------------------------------
Trial 3625
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=38,
random_state=100))])
cv score: [0.75287356 0.62931034 0.65517241 0.67700258 0.43410853]
----------------------------------------
Trial 3626
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=162,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.625 0.68678161 0.55890805 0.5749354 0.46124031]
----------------------------------------
Trial 3627
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=58,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.58477011 0.63074713 0.55297158 0.37209302]
----------------------------------------
Trial 3628
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=166, random_state=100))])
cv score: [0.59482759 0.6795977 0.59051724 0.63824289 0.4379845 ]
----------------------------------------
Trial 3629
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=148,
random_state=100))])
cv score: [0.62356322 0.66954023 0.60344828 0.67700258 0.47932817]
----------------------------------------
Trial 3630
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78663793 0.55890805 0.69252874 0.57041344 0.40891473]
----------------------------------------
Trial 3631
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=145,
random_state=100))])
cv score: [0.64224138 0.66091954 0.59051724 0.64599483 0.4379845 ]
----------------------------------------
Trial 3632
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='sqrt',
n_estimators=40, random_state=100,
subsample=0.95))])
cv score: [0.52442529 0.75431034 0.47988506 0.61369509 0.45736434]
----------------------------------------
Trial 3633
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65948276 0.65373563 0.64798851 0.66925065 0.41731266]
----------------------------------------
Trial 3634
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=48,
random_state=100))])
cv score: [0.62356322 0.63793103 0.61925287 0.68992248 0.36046512]
----------------------------------------
Trial 3635
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
n_estimators=137, random_state=100,
subsample=0.85))])
cv score: [0.56896552 0.70977011 0.60775862 0.75322997 0.43410853]
----------------------------------------
Trial 3636
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=179,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.71264368 0.58045977 0.59173127 0.4005168 ]
----------------------------------------
Trial 3637
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=151,
random_state=100,
subsample=0.85))])
cv score: [0.56752874 0.68821839 0.47701149 0.67312661 0.42118863]
----------------------------------------
Trial 3638
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='sqrt',
n_estimators=188, random_state=100,
subsample=0.9))])
cv score: [0.52442529 0.70545977 0.56465517 0.64857881 0.36950904]
----------------------------------------
Trial 3639
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=36,
random_state=100))])
cv score: [0.5704023 0.70689655 0.54310345 0.7002584 0.37596899]
----------------------------------------
Trial 3640
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=195, random_state=100))])
cv score: [0.59770115 0.72988506 0.6408046 0.67829457 0.43669251]
----------------------------------------
Trial 3641
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=144,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.625 0.70545977 0.65229885 0.56459948 0.38242894]
----------------------------------------
Trial 3642
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=106,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61781609 0.75431034 0.55316092 0.56847545 0.43540052]
----------------------------------------
Trial 3643
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=75, random_state=100))])
cv score: [0.57112069 0.72772989 0.65517241 0.6505168 0.45155039]
----------------------------------------
Trial 3644
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=68,
random_state=100))])
cv score: [0.5933908 0.66666667 0.625 0.69896641 0.40891473]
----------------------------------------
Trial 3645
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='sqrt',
n_estimators=139,
random_state=100))])
cv score: [0.51867816 0.58477011 0.48563218 0.6369509 0.44702842]
----------------------------------------
Trial 3646
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=125,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79382184 0.59913793 0.63577586 0.62984496 0.43669251]
----------------------------------------
Trial 3647
----------------------------------------
Parameters {'gb__n_estimators': 40, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='sqrt',
n_estimators=40, random_state=100,
subsample=0.85))])
cv score: [0.66091954 0.48275862 0.64655172 0.65633075 0.46124031]
----------------------------------------
Trial 3648
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=20, random_state=100))])
cv score: [0.69827586 0.625 0.49568966 0.64211886 0.36175711]
----------------------------------------
Trial 3649
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=90,
random_state=100))])
cv score: [0.61278736 0.6012931 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 3650
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79094828 0.58548851 0.68678161 0.60465116 0.42118863]
----------------------------------------
Trial 3651
----------------------------------------
Parameters {'rf__n_estimators': 120, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=120,
random_state=100))])
cv score: [0.59770115 0.67241379 0.63649425 0.65374677 0.43281654]
----------------------------------------
Trial 3652
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=44, random_state=100,
subsample=0.7))])
cv score: [0.78017241 0.69396552 0.66954023 0.67183463 0.36821705]
----------------------------------------
Trial 3653
----------------------------------------
Parameters {'rf__n_estimators': 22, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=22, random_state=100))])
cv score: [0.68678161 0.70402299 0.52011494 0.53552972 0.39728682]
----------------------------------------
Trial 3654
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='log2',
n_estimators=104, random_state=100,
subsample=0.95))])
cv score: [0.56178161 0.71695402 0.61781609 0.68863049 0.39018088]
----------------------------------------
Trial 3655
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=136,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56321839 0.72557471 0.64511494 0.59043928 0.43927649]
----------------------------------------
Trial 3656
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.625 0.64798851 0.64082687 0.39018088]
----------------------------------------
Trial 3657
----------------------------------------
Parameters {'gb__n_estimators': 103, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=103,
random_state=100))])
cv score: [0.56465517 0.71695402 0.61206897 0.63436693 0.4502584 ]
----------------------------------------
Trial 3658
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
n_estimators=121, random_state=100,
subsample=0.65))])
cv score: [0.55603448 0.70689655 0.60344828 0.74031008 0.42764858]
----------------------------------------
Trial 3659
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='sqrt',
n_estimators=53, random_state=100,
subsample=0.9))])
cv score: [0.52442529 0.71695402 0.47413793 0.63953488 0.44444444]
----------------------------------------
Trial 3660
----------------------------------------
Parameters {'xgb__n_estimators': 66, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=66,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.61206897 0.47126437 0.54392765 0.34625323]
----------------------------------------
Trial 3661
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=39,
random_state=100))])
cv score: [0.6408046 0.69683908 0.62931034 0.71576227 0.39664083]
----------------------------------------
Trial 3662
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=138, random_state=100,
subsample=0.65))])
cv score: [0.74425287 0.67816092 0.66235632 0.65245478 0.49870801]
----------------------------------------
Trial 3663
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=72, random_state=100))])
cv score: [0.64942529 0.63505747 0.48706897 0.6744186 0.40180879]
----------------------------------------
Trial 3664
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=106,
random_state=100))])
cv score: [0.61350575 0.59841954 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 3665
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.63362069 0.57183908 0.58139535 0.47416021]
----------------------------------------
Trial 3666
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=90,
random_state=100))])
cv score: [0.61278736 0.6012931 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 3667
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=193,
random_state=100))])
cv score: [0.76724138 0.63936782 0.6954023 0.63824289 0.39147287]
----------------------------------------
Trial 3668
----------------------------------------
Parameters {'rf__n_estimators': 193, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=193, random_state=100))])
cv score: [0.73563218 0.62356322 0.65373563 0.62273902 0.41731266]
----------------------------------------
Trial 3669
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=21, random_state=100,
subsample=0.65))])
cv score: [0.4454023 0.51005747 0.47988506 0.71059432 0.47932817]
----------------------------------------
Trial 3670
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=194,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5387931 0.60344828 0.75287356 0.58397933 0.40568475]
----------------------------------------
Trial 3671
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=80, random_state=100))])
cv score: [0.54885057 0.64655172 0.60632184 0.57622739 0.4005168 ]
----------------------------------------
Trial 3672
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=56,
random_state=100))])
cv score: [0.79238506 0.61925287 0.70761494 0.62144703 0.3869509 ]
----------------------------------------
Trial 3673
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=22,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77155172 0.58979885 0.49928161 0.52777778 0.36627907]
----------------------------------------
Trial 3674
----------------------------------------
Parameters {'gb__n_estimators': 20, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=20, random_state=100,
subsample=0.8))])
cv score: [0.57758621 0.67816092 0.64655172 0.58139535 0.39664083]
----------------------------------------
Trial 3675
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.79741379 0.61206897 0.59431525 0.44832041]
----------------------------------------
Trial 3676
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='sqrt',
n_estimators=61,
random_state=100))])
cv score: [0.71264368 0.68965517 0.7341954 0.65245478 0.46899225]
----------------------------------------
Trial 3677
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=90, random_state=100,
subsample=0.85))])
cv score: [0.5316092 0.57183908 0.47270115 0.61369509 0.45348837]
----------------------------------------
Trial 3678
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=132,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.73563218 0.58764368 0.54134367 0.46382429]
----------------------------------------
Trial 3679
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='sqrt',
n_estimators=38,
random_state=100))])
cv score: [0.58189655 0.7183908 0.59913793 0.61757106 0.50775194]
----------------------------------------
Trial 3680
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74640805 0.58548851 0.51221264 0.54134367 0.37080103]
----------------------------------------
Trial 3681
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=50, random_state=100))])
cv score: [0.56178161 0.70043103 0.63577586 0.70994832 0.43540052]
----------------------------------------
Trial 3682
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=73,
random_state=100))])
cv score: [0.60344828 0.6795977 0.61063218 0.70671835 0.39599483]
----------------------------------------
Trial 3683
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=172,
random_state=100))])
cv score: [0.64008621 0.59985632 0.56034483 0.51744186 0.41860465]
----------------------------------------
Trial 3684
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=165,
random_state=100))])
cv score: [0.64942529 0.66522989 0.58045977 0.64082687 0.43281654]
----------------------------------------
Trial 3685
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=186,
random_state=100))])
cv score: [0.63362069 0.66666667 0.57758621 0.63953488 0.42764858]
----------------------------------------
Trial 3686
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=53,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74856322 0.61063218 0.74137931 0.61434109 0.40633075]
----------------------------------------
Trial 3687
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='sqrt',
n_estimators=48, random_state=100,
subsample=0.6))])
cv score: [0.66810345 0.64942529 0.67816092 0.62144703 0.34625323]
----------------------------------------
Trial 3688
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=126, random_state=100,
subsample=0.7))])
cv score: [0.6637931 0.68678161 0.49856322 0.65374677 0.44832041]
----------------------------------------
Trial 3689
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=10,
random_state=100))])
cv score: [0.6170977 0.59698276 0.5545977 0.5755814 0.45348837]
----------------------------------------
Trial 3690
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=80, random_state=100,
subsample=0.7))])
cv score: [0.55172414 0.60775862 0.76867816 0.60206718 0.35400517]
----------------------------------------
Trial 3691
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=38, random_state=100))])
cv score: [0.67097701 0.68175287 0.61925287 0.66149871 0.45865633]
----------------------------------------
Trial 3692
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=78, random_state=100))])
cv score: [0.58333333 0.72126437 0.65086207 0.66343669 0.42700258]
----------------------------------------
Trial 3693
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=159,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.68534483 0.55172414 0.59560724 0.43152455]
----------------------------------------
Trial 3694
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=78,
random_state=100))])
cv score: [0.65229885 0.625 0.63505747 0.62919897 0.42248062]
----------------------------------------
Trial 3695
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
n_estimators=194, random_state=100,
subsample=0.9))])
cv score: [0.65517241 0.72844828 0.59482759 0.66408269 0.41602067]
----------------------------------------
Trial 3696
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=72,
random_state=100))])
cv score: [0.5933908 0.63721264 0.625 0.69121447 0.38113695]
----------------------------------------
Trial 3697
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
n_estimators=161, random_state=100,
subsample=0.85))])
cv score: [0.72772989 0.68103448 0.54956897 0.65245478 0.40956072]
----------------------------------------
Trial 3698
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=182,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.64942529 0.67241379 0.68863049 0.42118863]
----------------------------------------
Trial 3699
----------------------------------------
Parameters {'rf__n_estimators': 59, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=59, random_state=100))])
cv score: [0.61063218 0.65517241 0.66954023 0.58914729 0.42764858]
----------------------------------------
Trial 3700
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=87,
random_state=100))])
cv score: [0.56609195 0.69109195 0.50574713 0.6369509 0.46899225]
----------------------------------------
Trial 3701
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=176,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 3702
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='log2',
n_estimators=156, random_state=100,
subsample=0.9))])
cv score: [0.59051724 0.66954023 0.57902299 0.7118863 0.46899225]
----------------------------------------
Trial 3703
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=176,
random_state=100))])
cv score: [0.6408046 0.67241379 0.57183908 0.63953488 0.43023256]
----------------------------------------
Trial 3704
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=71, random_state=100))])
cv score: [0.73563218 0.63074713 0.69252874 0.56589147 0.39018088]
----------------------------------------
Trial 3705
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=63,
random_state=100))])
cv score: [0.58692529 0.60416667 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 3706
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=174,
random_state=100))])
cv score: [0.70258621 0.65229885 0.66954023 0.65891473 0.42635659]
----------------------------------------
Trial 3707
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.70977011 0.5316092 0.69250646 0.44832041]
----------------------------------------
Trial 3708
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=165,
random_state=100))])
cv score: [0.64942529 0.66522989 0.58045977 0.64082687 0.43281654]
----------------------------------------
Trial 3709
----------------------------------------
Parameters {'gb__n_estimators': 64, 'gb__subsample': 0.6, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=64, random_state=100,
subsample=0.6))])
cv score: [0.52442529 0.5387931 0.55172414 0.50516796 0.41731266]
----------------------------------------
Trial 3710
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.6))])
cv score: [0.68390805 0.63936782 0.45689655 0.65891473 0.55426357]
----------------------------------------
Trial 3711
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=191, random_state=100))])
cv score: [0.56896552 0.67528736 0.62068966 0.65374677 0.43087855]
----------------------------------------
Trial 3712
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54166667 0.72701149 0.54454023 0.59302326 0.45348837]
----------------------------------------
Trial 3713
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.64367816 0.65948276 0.62919897 0.4121447 ]
----------------------------------------
Trial 3714
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=15,
random_state=100))])
cv score: [0.63505747 0.60201149 0.48706897 0.67054264 0.42894057]
----------------------------------------
Trial 3715
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='log2',
n_estimators=14, random_state=100,
subsample=0.9))])
cv score: [0.8045977 0.60344828 0.75646552 0.67829457 0.41472868]
----------------------------------------
Trial 3716
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='log2',
n_estimators=61, random_state=100,
subsample=0.8))])
cv score: [0.55747126 0.60201149 0.58045977 0.6369509 0.48966408]
----------------------------------------
Trial 3717
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=6,
max_features='log2',
n_estimators=91, random_state=100,
subsample=0.7))])
cv score: [0.65229885 0.63362069 0.61206897 0.65891473 0.42248062]
----------------------------------------
Trial 3718
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=160, random_state=100,
subsample=0.75))])
cv score: [0.58045977 0.66091954 0.54885057 0.67183463 0.47157623]
----------------------------------------
Trial 3719
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=197,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56465517 0.72557471 0.60057471 0.5749354 0.38501292]
----------------------------------------
Trial 3720
----------------------------------------
Parameters {'xgb__n_estimators': 182, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=182,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60201149 0.70689655 0.56609195 0.62919897 0.43023256]
----------------------------------------
Trial 3721
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.68534483 0.66522989 0.65116279 0.45219638]
----------------------------------------
Trial 3722
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71408046 0.57614943 0.64511494 0.55813953 0.39341085]
----------------------------------------
Trial 3723
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69612069 0.68175287 0.66666667 0.5878553 0.3998708 ]
----------------------------------------
Trial 3724
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='sqrt',
n_estimators=176, random_state=100,
subsample=0.85))])
cv score: [0.62643678 0.45402299 0.55316092 0.58397933 0.45219638]
----------------------------------------
Trial 3725
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=79, random_state=100,
subsample=0.6))])
cv score: [0.6795977 0.60201149 0.54166667 0.5503876 0.31524548]
----------------------------------------
Trial 3726
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=90, random_state=100,
subsample=0.95))])
cv score: [0.71982759 0.67385057 0.69252874 0.6744186 0.38372093]
----------------------------------------
Trial 3727
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='log2',
n_estimators=70, random_state=100,
subsample=0.7))])
cv score: [0.65517241 0.70258621 0.44109195 0.67183463 0.45607235]
----------------------------------------
Trial 3728
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='sqrt',
n_estimators=174,
random_state=100))])
cv score: [0.60344828 0.68103448 0.58908046 0.67054264 0.47932817]
----------------------------------------
Trial 3729
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=77,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.66666667 0.6954023 0.61886305 0.40180879]
----------------------------------------
Trial 3730
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=127,
random_state=100))])
cv score: [0.61206897 0.68103448 0.61063218 0.71705426 0.42764858]
----------------------------------------
Trial 3731
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=11,
max_features='log2',
n_estimators=91, random_state=100,
subsample=0.85))])
cv score: [0.64224138 0.56034483 0.48994253 0.6124031 0.48320413]
----------------------------------------
Trial 3732
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=24, random_state=100))])
cv score: [0.73132184 0.68678161 0.60057471 0.60788114 0.47739018]
----------------------------------------
Trial 3733
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=175,
random_state=100))])
cv score: [0.6408046 0.59985632 0.56034483 0.51744186 0.41860465]
----------------------------------------
Trial 3734
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='log2',
n_estimators=46, random_state=100,
subsample=0.65))])
cv score: [0.60057471 0.62787356 0.43534483 0.60981912 0.49224806]
----------------------------------------
Trial 3735
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=25,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.58189655 0.43534483 0.56976744 0.44056848]
----------------------------------------
Trial 3736
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=94, random_state=100,
subsample=0.6))])
cv score: [0.6566092 0.5933908 0.50143678 0.62015504 0.44186047]
----------------------------------------
Trial 3737
----------------------------------------
Parameters {'rf__n_estimators': 144, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=144, random_state=100))])
cv score: [0.60201149 0.70761494 0.66810345 0.69379845 0.41860465]
----------------------------------------
Trial 3738
----------------------------------------
Parameters {'xgb__n_estimators': 179, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=179,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7887931 0.67528736 0.65229885 0.65374677 0.44379845]
----------------------------------------
Trial 3739
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=140, random_state=100,
subsample=0.95))])
cv score: [0.55747126 0.73563218 0.58764368 0.63307494 0.40568475]
----------------------------------------
Trial 3740
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=119,
random_state=100))])
cv score: [0.47126437 0.67816092 0.41810345 0.74935401 0.52583979]
----------------------------------------
Trial 3741
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=62,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59913793 0.70258621 0.7341954 0.64728682 0.40762274]
----------------------------------------
Trial 3742
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=97,
random_state=100))])
cv score: [0.64152299 0.60272989 0.56034483 0.51744186 0.41860465]
----------------------------------------
Trial 3743
----------------------------------------
Parameters {'gb__n_estimators': 79, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=79, random_state=100,
subsample=0.8))])
cv score: [0.58045977 0.68247126 0.5704023 0.64082687 0.4754522 ]
----------------------------------------
Trial 3744
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.95))])
cv score: [0.7816092 0.65373563 0.75431034 0.56072351 0.36950904]
----------------------------------------
Trial 3745
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=137,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 3746
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68821839 0.59195402 0.6875 0.59237726 0.43410853]
----------------------------------------
Trial 3747
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=175, random_state=100,
subsample=0.75))])
cv score: [0.58477011 0.66954023 0.50287356 0.69250646 0.44186047]
----------------------------------------
Trial 3748
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=151, random_state=100))])
cv score: [0.61925287 0.69683908 0.67241379 0.68346253 0.42958656]
----------------------------------------
Trial 3749
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=142,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78017241 0.63146552 0.68821839 0.5626615 0.36627907]
----------------------------------------
Trial 3750
----------------------------------------
Parameters {'rf__n_estimators': 195, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=195, random_state=100))])
cv score: [0.6566092 0.67816092 0.60201149 0.66666667 0.43540052]
----------------------------------------
Trial 3751
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=178, random_state=100))])
cv score: [0.64224138 0.66235632 0.60488506 0.61369509 0.46124031]
----------------------------------------
Trial 3752
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=51,
random_state=100))])
cv score: [0.70258621 0.6408046 0.66954023 0.60335917 0.37080103]
----------------------------------------
Trial 3753
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=46,
random_state=100))])
cv score: [0.69396552 0.63936782 0.63362069 0.6124031 0.36950904]
----------------------------------------
Trial 3754
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='sqrt',
n_estimators=19, random_state=100,
subsample=0.85))])
cv score: [0.65373563 0.6408046 0.42385057 0.54780362 0.48062016]
----------------------------------------
Trial 3755
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=196, random_state=100))])
cv score: [0.6091954 0.7112069 0.6637931 0.68346253 0.43927649]
----------------------------------------
Trial 3756
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=44, random_state=100))])
cv score: [0.54454023 0.62643678 0.66810345 0.60852713 0.40568475]
----------------------------------------
Trial 3757
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.68534483 0.66091954 0.66149871 0.45607235]
----------------------------------------
Trial 3758
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=184, random_state=100))])
cv score: [0.58836207 0.67528736 0.58333333 0.61886305 0.42829457]
----------------------------------------
Trial 3759
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='sqrt',
n_estimators=126, random_state=100,
subsample=0.65))])
cv score: [0.73706897 0.61350575 0.65086207 0.62273902 0.41989664]
----------------------------------------
Trial 3760
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53304598 0.71408046 0.58045977 0.63953488 0.40439276]
----------------------------------------
Trial 3761
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=44,
random_state=100))])
cv score: [0.78951149 0.61997126 0.71264368 0.63824289 0.38049096]
----------------------------------------
Trial 3762
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=87,
random_state=100))])
cv score: [0.6170977 0.60057471 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 3763
----------------------------------------
Parameters {'rf__n_estimators': 94, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=94, random_state=100))])
cv score: [0.56178161 0.68462644 0.58261494 0.60529716 0.46124031]
----------------------------------------
Trial 3764
----------------------------------------
Parameters {'rf__n_estimators': 183, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=183,
random_state=100))])
cv score: [0.75574713 0.64798851 0.69252874 0.64599483 0.44186047]
----------------------------------------
Trial 3765
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=50, random_state=100,
subsample=0.95))])
cv score: [0.50718391 0.63362069 0.55172414 0.62790698 0.37080103]
----------------------------------------
Trial 3766
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.64367816 0.53591954 0.52583979 0.39276486]
----------------------------------------
Trial 3767
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=178, random_state=100))])
cv score: [0.64224138 0.64942529 0.58333333 0.68217054 0.38372093]
----------------------------------------
Trial 3768
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54597701 0.73850575 0.58333333 0.62790698 0.38372093]
----------------------------------------
Trial 3769
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=125,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56752874 0.73706897 0.60344828 0.64728682 0.45607235]
----------------------------------------
Trial 3770
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=55,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75431034 0.60057471 0.66522989 0.58591731 0.37144703]
----------------------------------------
Trial 3771
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=104, random_state=100,
subsample=0.6))])
cv score: [0.73275862 0.64942529 0.62643678 0.62919897 0.44056848]
----------------------------------------
Trial 3772
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=23,
random_state=100))])
cv score: [0.69396552 0.61494253 0.59051724 0.67054264 0.41085271]
----------------------------------------
Trial 3773
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='log2',
n_estimators=15, random_state=100,
subsample=0.75))])
cv score: [0.69971264 0.70545977 0.49568966 0.5749354 0.39793282]
----------------------------------------
Trial 3774
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=99, random_state=100))])
cv score: [0.65804598 0.63362069 0.5704023 0.58397933 0.4250646 ]
----------------------------------------
Trial 3775
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=199,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.64655172 0.55747126 0.65633075 0.42764858]
----------------------------------------
Trial 3776
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=85,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74281609 0.56609195 0.68462644 0.55620155 0.41989664]
----------------------------------------
Trial 3777
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='sqrt',
n_estimators=69, random_state=100,
subsample=0.7))])
cv score: [0.57327586 0.67025862 0.61925287 0.69896641 0.55943152]
----------------------------------------
Trial 3778
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=194, random_state=100))])
cv score: [0.72413793 0.6408046 0.65517241 0.57881137 0.41472868]
----------------------------------------
Trial 3779
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60201149 0.67097701 0.56896552 0.62144703 0.38242894]
----------------------------------------
Trial 3780
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=124, random_state=100))])
cv score: [0.55890805 0.67744253 0.61135057 0.59689922 0.4005168 ]
----------------------------------------
Trial 3781
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=23,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68821839 0.57255747 0.58836207 0.57170543 0.43410853]
----------------------------------------
Trial 3782
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=43,
random_state=100))])
cv score: [0.6566092 0.63074713 0.65086207 0.60077519 0.44315245]
----------------------------------------
Trial 3783
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.6637931 0.6091954 0.62790698 0.43927649]
----------------------------------------
Trial 3784
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.625 0.58764368 0.62212644 0.60206718 0.41085271]
----------------------------------------
Trial 3785
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=169,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64511494 0.6795977 0.6566092 0.64211886 0.42377261]
----------------------------------------
Trial 3786
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=80,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.65804598 0.70833333 0.62661499 0.40439276]
----------------------------------------
Trial 3787
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=36, random_state=100))])
cv score: [0.74137931 0.67672414 0.6091954 0.61369509 0.49612403]
----------------------------------------
Trial 3788
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='log2',
n_estimators=166, random_state=100,
subsample=0.8))])
cv score: [0.73850575 0.64367816 0.6408046 0.62919897 0.43669251]
----------------------------------------
Trial 3789
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, n_estimators=152,
random_state=100, subsample=0.8))])
cv score: [0.56896552 0.69109195 0.59051724 0.62403101 0.44573643]
----------------------------------------
Trial 3790
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=56,
random_state=100))])
cv score: [0.60775862 0.62859195 0.64367816 0.67894057 0.3875969 ]
----------------------------------------
Trial 3791
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=141, random_state=100))])
cv score: [0.57902299 0.65086207 0.5933908 0.64341085 0.43927649]
----------------------------------------
Trial 3792
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=126, random_state=100))])
cv score: [0.64511494 0.64942529 0.61350575 0.67312661 0.38888889]
----------------------------------------
Trial 3793
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=89, random_state=100,
subsample=0.75))])
cv score: [0.72988506 0.71264368 0.65229885 0.61757106 0.43152455]
----------------------------------------
Trial 3794
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69252874 0.62356322 0.54885057 0.625323 0.49612403]
----------------------------------------
Trial 3795
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=46, random_state=100))])
cv score: [0.53591954 0.67313218 0.57112069 0.58850129 0.47674419]
----------------------------------------
Trial 3796
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=74, random_state=100))])
cv score: [0.6408046 0.59913793 0.57183908 0.59560724 0.4379845 ]
----------------------------------------
Trial 3797
----------------------------------------
Parameters {'rf__n_estimators': 55, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=55,
random_state=100))])
cv score: [0.65229885 0.63793103 0.56178161 0.57235142 0.36757106]
----------------------------------------
Trial 3798
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='log2',
n_estimators=118,
random_state=100))])
cv score: [0.60057471 0.68534483 0.41522989 0.5878553 0.42377261]
----------------------------------------
Trial 3799
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='sqrt',
n_estimators=189, random_state=100,
subsample=0.65))])
cv score: [0.62931034 0.68821839 0.64511494 0.71317829 0.41343669]
----------------------------------------
Trial 3800
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=88,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.67241379 0.6408046 0.61886305 0.40180879]
----------------------------------------
Trial 3801
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=73,
random_state=100))])
cv score: [0.7112069 0.64511494 0.67528736 0.60077519 0.37338501]
----------------------------------------
Trial 3802
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=28, random_state=100,
subsample=0.85))])
cv score: [0.6566092 0.57327586 0.625 0.625323 0.52325581]
----------------------------------------
Trial 3803
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='log2',
n_estimators=124, random_state=100,
subsample=0.6))])
cv score: [0.69109195 0.61350575 0.63074713 0.59689922 0.45348837]
----------------------------------------
Trial 3804
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=57, random_state=100))])
cv score: [0.5704023 0.60488506 0.64798851 0.64728682 0.38242894]
----------------------------------------
Trial 3805
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=36, random_state=100,
subsample=0.7))])
cv score: [0.67097701 0.63936782 0.57614943 0.6124031 0.39018088]
----------------------------------------
Trial 3806
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=104, random_state=100))])
cv score: [0.62428161 0.70761494 0.6408046 0.66731266 0.42183463]
----------------------------------------
Trial 3807
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58908046 0.69827586 0.60344828 0.59173127 0.37338501]
----------------------------------------
Trial 3808
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=89,
random_state=100))])
cv score: [0.60632184 0.67672414 0.6487069 0.70542636 0.36627907]
----------------------------------------
Trial 3809
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=76,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73994253 0.66666667 0.70402299 0.59496124 0.4748062 ]
----------------------------------------
Trial 3810
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73275862 0.66666667 0.69971264 0.58139535 0.4618863 ]
----------------------------------------
Trial 3811
----------------------------------------
Parameters {'gb__n_estimators': 140, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='log2',
n_estimators=140, random_state=100,
subsample=0.9))])
cv score: [0.59051724 0.68965517 0.49137931 0.58010336 0.47674419]
----------------------------------------
Trial 3812
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=125,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.7183908 0.55316092 0.61111111 0.39147287]
----------------------------------------
Trial 3813
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=44,
random_state=100))])
cv score: [0.52514368 0.59195402 0.56752874 0.65891473 0.38372093]
----------------------------------------
Trial 3814
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=89, random_state=100,
subsample=0.8))])
cv score: [0.48132184 0.51005747 0.64798851 0.62273902 0.54780362]
----------------------------------------
Trial 3815
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=189,
random_state=100))])
cv score: [0.54741379 0.65517241 0.59195402 0.73643411 0.40310078]
----------------------------------------
Trial 3816
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=160, random_state=100,
subsample=0.75))])
cv score: [0.58189655 0.6408046 0.48994253 0.58527132 0.45478036]
----------------------------------------
Trial 3817
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=13,
max_features='log2',
n_estimators=34, random_state=100,
subsample=0.65))])
cv score: [0.46695402 0.74568966 0.42241379 0.6369509 0.4379845 ]
----------------------------------------
Trial 3818
----------------------------------------
Parameters {'gb__n_estimators': 26, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=26, random_state=100,
subsample=0.8))])
cv score: [0.53017241 0.56752874 0.47701149 0.61111111 0.61886305]
----------------------------------------
Trial 3819
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=50,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77083333 0.57902299 0.74856322 0.55426357 0.40956072]
----------------------------------------
Trial 3820
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=161, random_state=100,
subsample=0.7))])
cv score: [0.64798851 0.64942529 0.5387931 0.71317829 0.45478036]
----------------------------------------
Trial 3821
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=83,
random_state=100))])
cv score: [0.75143678 0.63218391 0.70545977 0.64470284 0.41602067]
----------------------------------------
Trial 3822
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=9,
max_features='sqrt',
n_estimators=22, random_state=100,
subsample=0.65))])
cv score: [0.54597701 0.52586207 0.52873563 0.61498708 0.61111111]
----------------------------------------
Trial 3823
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=73, random_state=100))])
cv score: [0.61063218 0.68390805 0.67528736 0.68863049 0.44702842]
----------------------------------------
Trial 3824
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=171, random_state=100))])
cv score: [0.625 0.68965517 0.61925287 0.68475452 0.43152455]
----------------------------------------
Trial 3825
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=111,
random_state=100))])
cv score: [0.61637931 0.59770115 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 3826
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=198, random_state=100))])
cv score: [0.74712644 0.65086207 0.64224138 0.63824289 0.4121447 ]
----------------------------------------
Trial 3827
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=117,
random_state=100))])
cv score: [0.78448276 0.63074713 0.66738506 0.625323 0.40310078]
----------------------------------------
Trial 3828
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
n_estimators=168, random_state=100,
subsample=0.95))])
cv score: [0.61637931 0.70545977 0.54597701 0.79069767 0.45865633]
----------------------------------------
Trial 3829
----------------------------------------
Parameters {'gb__n_estimators': 175, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='log2',
n_estimators=175, random_state=100,
subsample=0.85))])
cv score: [0.625 0.70689655 0.48132184 0.54909561 0.52583979]
----------------------------------------
Trial 3830
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=172,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.67097701 0.58477011 0.57751938 0.40439276]
----------------------------------------
Trial 3831
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=193,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5704023 0.6091954 0.59051724 0.64211886 0.42118863]
----------------------------------------
Trial 3832
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=79, random_state=100))])
cv score: [0.61781609 0.64367816 0.61781609 0.62919897 0.39534884]
----------------------------------------
Trial 3833
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=68,
random_state=100))])
cv score: [0.61063218 0.69827586 0.68103448 0.66149871 0.42377261]
----------------------------------------
Trial 3834
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=93, random_state=100))])
cv score: [0.73850575 0.60775862 0.58189655 0.58656331 0.42764858]
----------------------------------------
Trial 3835
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=36, random_state=100,
subsample=0.85))])
cv score: [0.59913793 0.5933908 0.56178161 0.68346253 0.49612403]
----------------------------------------
Trial 3836
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=15, random_state=100))])
cv score: [0.7112069 0.63505747 0.33117816 0.60788114 0.39211886]
----------------------------------------
Trial 3837
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.64798851 0.6637931 0.5878553 0.43281654]
----------------------------------------
Trial 3838
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=27, random_state=100,
subsample=0.8))])
cv score: [0.62356322 0.67528736 0.56609195 0.48449612 0.60594315]
----------------------------------------
Trial 3839
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.67528736 0.65948276 0.62273902 0.39793282]
----------------------------------------
Trial 3840
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=145,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 3841
----------------------------------------
Parameters {'rf__n_estimators': 70, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=70,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 3842
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.73994253 0.59913793 0.62144703 0.43023256]
----------------------------------------
Trial 3843
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=110, random_state=100))])
cv score: [0.66666667 0.66091954 0.60344828 0.60206718 0.41989664]
----------------------------------------
Trial 3844
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=110, random_state=100,
subsample=0.85))])
cv score: [0.62212644 0.63793103 0.53304598 0.58397933 0.4379845 ]
----------------------------------------
Trial 3845
----------------------------------------
Parameters {'gb__n_estimators': 162, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='log2',
n_estimators=162,
random_state=100))])
cv score: [0.70258621 0.69109195 0.6408046 0.62790698 0.44444444]
----------------------------------------
Trial 3846
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.7))])
cv score: [0.75143678 0.65517241 0.70545977 0.66666667 0.42764858]
----------------------------------------
Trial 3847
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=128,
random_state=100))])
cv score: [0.65948276 0.6408046 0.6566092 0.65374677 0.45865633]
----------------------------------------
Trial 3848
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=122,
random_state=100))])
cv score: [0.58692529 0.60272989 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 3849
----------------------------------------
Parameters {'rf__n_estimators': 122, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=122, random_state=100))])
cv score: [0.76149425 0.64511494 0.65517241 0.62919897 0.4005168 ]
----------------------------------------
Trial 3850
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
n_estimators=159, random_state=100,
subsample=0.95))])
cv score: [0.51005747 0.69827586 0.55890805 0.74806202 0.39018088]
----------------------------------------
Trial 3851
----------------------------------------
Parameters {'gb__n_estimators': 104, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=104, random_state=100,
subsample=0.8))])
cv score: [0.66666667 0.68534483 0.62787356 0.65116279 0.41085271]
----------------------------------------
Trial 3852
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.67241379 0.5862069 0.56330749 0.4250646 ]
----------------------------------------
Trial 3853
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=123, random_state=100))])
cv score: [0.76724138 0.63074713 0.64511494 0.63049096 0.41731266]
----------------------------------------
Trial 3854
----------------------------------------
Parameters {'xgb__n_estimators': 164, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=164,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.69683908 0.73275862 0.60852713 0.40826873]
----------------------------------------
Trial 3855
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=152,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 3856
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=150,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74568966 0.67097701 0.70761494 0.64147287 0.43217054]
----------------------------------------
Trial 3857
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=196,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.71982759 0.5704023 0.62919897 0.42635659]
----------------------------------------
Trial 3858
----------------------------------------
Parameters {'rf__n_estimators': 141, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=141, random_state=100))])
cv score: [0.63074713 0.68534483 0.61637931 0.68475452 0.42118863]
----------------------------------------
Trial 3859
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=23,
random_state=100))])
cv score: [0.60488506 0.63505747 0.57974138 0.63630491 0.39341085]
----------------------------------------
Trial 3860
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=133,
random_state=100))])
cv score: [0.69971264 0.65086207 0.64942529 0.6498708 0.42635659]
----------------------------------------
Trial 3861
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=50, random_state=100))])
cv score: [0.6637931 0.61637931 0.47557471 0.62790698 0.41989664]
----------------------------------------
Trial 3862
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=154,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80962644 0.56178161 0.65158046 0.52842377 0.40245478]
----------------------------------------
Trial 3863
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=13, random_state=100))])
cv score: [0.71264368 0.6408046 0.32543103 0.59689922 0.42118863]
----------------------------------------
Trial 3864
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=57, random_state=100))])
cv score: [0.71264368 0.66954023 0.56178161 0.54909561 0.34754522]
----------------------------------------
Trial 3865
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='sqrt',
n_estimators=68, random_state=100,
subsample=0.6))])
cv score: [0.61206897 0.62212644 0.63649425 0.64082687 0.40826873]
----------------------------------------
Trial 3866
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=132, random_state=100))])
cv score: [0.60057471 0.67241379 0.59770115 0.64211886 0.43669251]
----------------------------------------
Trial 3867
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=195,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67385057 0.6408046 0.63936782 0.62403101 0.40956072]
----------------------------------------
Trial 3868
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
n_estimators=117, random_state=100,
subsample=0.65))])
cv score: [0.54310345 0.71982759 0.61637931 0.69121447 0.45994832]
----------------------------------------
Trial 3869
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=149, random_state=100,
subsample=0.9))])
cv score: [0.55316092 0.74281609 0.5704023 0.7002584 0.3501292 ]
----------------------------------------
Trial 3870
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=177,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.66235632 0.5933908 0.58268734 0.42377261]
----------------------------------------
Trial 3871
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=62, random_state=100,
subsample=0.8))])
cv score: [0.54454023 0.72701149 0.56321839 0.70671835 0.45607235]
----------------------------------------
Trial 3872
----------------------------------------
Parameters {'rf__n_estimators': 27, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=27, random_state=100))])
cv score: [0.70977011 0.66235632 0.65804598 0.50710594 0.43281654]
----------------------------------------
Trial 3873
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=141,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7966954 0.63649425 0.63505747 0.59173127 0.45607235]
----------------------------------------
Trial 3874
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=88,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77298851 0.56896552 0.68606322 0.6369509 0.43152455]
----------------------------------------
Trial 3875
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=141,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65229885 0.69827586 0.61637931 0.59302326 0.39664083]
----------------------------------------
Trial 3876
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74137931 0.55603448 0.53448276 0.67958656 0.40826873]
----------------------------------------
Trial 3877
----------------------------------------
Parameters {'xgb__n_estimators': 187, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=187,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.68821839 0.66666667 0.65374677 0.45090439]
----------------------------------------
Trial 3878
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='log2',
n_estimators=199, random_state=100,
subsample=0.7))])
cv score: [0.66522989 0.6408046 0.4295977 0.58010336 0.4870801 ]
----------------------------------------
Trial 3879
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74784483 0.60775862 0.69683908 0.60529716 0.4121447 ]
----------------------------------------
Trial 3880
----------------------------------------
Parameters {'rf__n_estimators': 123, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=123, random_state=100))])
cv score: [0.59841954 0.71623563 0.63074713 0.66472868 0.42829457]
----------------------------------------
Trial 3881
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=14, random_state=100))])
cv score: [0.69971264 0.63649425 0.31537356 0.56459948 0.40762274]
----------------------------------------
Trial 3882
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='sqrt',
n_estimators=155, random_state=100,
subsample=0.9))])
cv score: [0.71408046 0.65948276 0.6795977 0.64728682 0.41085271]
----------------------------------------
Trial 3883
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=164, random_state=100))])
cv score: [0.59482759 0.72270115 0.66091954 0.68992248 0.42571059]
----------------------------------------
Trial 3884
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=153,
random_state=100))])
cv score: [0.58692529 0.60272989 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 3885
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68678161 0.65804598 0.66954023 0.63307494 0.47028424]
----------------------------------------
Trial 3886
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='log2',
n_estimators=186, random_state=100,
subsample=0.95))])
cv score: [0.5704023 0.70402299 0.53448276 0.60594315 0.42894057]
----------------------------------------
Trial 3887
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74712644 0.67241379 0.70114943 0.62273902 0.44896641]
----------------------------------------
Trial 3888
----------------------------------------
Parameters {'xgb__n_estimators': 92, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=92,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69683908 0.63362069 0.72557471 0.65826873 0.43604651]
----------------------------------------
Trial 3889
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=35, random_state=100))])
cv score: [0.54166667 0.61781609 0.6795977 0.60400517 0.43669251]
----------------------------------------
Trial 3890
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=189,
random_state=100))])
cv score: [0.5862069 0.66954023 0.62212644 0.68346253 0.41860465]
----------------------------------------
Trial 3891
----------------------------------------
Parameters {'rf__n_estimators': 172, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=172, random_state=100))])
cv score: [0.62643678 0.69396552 0.61925287 0.68475452 0.43152455]
----------------------------------------
Trial 3892
----------------------------------------
Parameters {'rf__n_estimators': 146, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=146, random_state=100))])
cv score: [0.72126437 0.6408046 0.67241379 0.57751938 0.4121447 ]
----------------------------------------
Trial 3893
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=48, random_state=100))])
cv score: [0.65948276 0.62787356 0.47701149 0.64341085 0.42377261]
----------------------------------------
Trial 3894
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.65086207 0.55747126 0.60981912 0.43669251]
----------------------------------------
Trial 3895
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='sqrt',
n_estimators=179, random_state=100,
subsample=0.9))])
cv score: [0.73132184 0.65517241 0.66666667 0.63178295 0.40439276]
----------------------------------------
Trial 3896
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=151, random_state=100))])
cv score: [0.56178161 0.67887931 0.58189655 0.60788114 0.4502584 ]
----------------------------------------
Trial 3897
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=93,
random_state=100))])
cv score: [0.70689655 0.64798851 0.68534483 0.65633075 0.39276486]
----------------------------------------
Trial 3898
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.6566092 0.70258621 0.60465116 0.4121447 ]
----------------------------------------
Trial 3899
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='sqrt',
n_estimators=44, random_state=100,
subsample=0.6))])
cv score: [0.66522989 0.62212644 0.6795977 0.57364341 0.44056848]
----------------------------------------
Trial 3900
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=145,
random_state=100))])
cv score: [0.58979885 0.60272989 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 3901
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=75, random_state=100,
subsample=0.7))])
cv score: [0.59913793 0.63505747 0.52729885 0.63436693 0.48191214]
----------------------------------------
Trial 3902
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
max_features='log2',
n_estimators=118, random_state=100,
subsample=0.9))])
cv score: [0.62068966 0.62931034 0.48994253 0.58397933 0.43540052]
----------------------------------------
Trial 3903
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61781609 0.7887931 0.57902299 0.5994832 0.37338501]
----------------------------------------
Trial 3904
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.48850575 0.62356322 0.62931034 0.54780362 0.47803618]
----------------------------------------
Trial 3905
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74353448 0.62284483 0.69971264 0.56782946 0.44573643]
----------------------------------------
Trial 3906
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='log2',
n_estimators=116,
random_state=100))])
cv score: [0.63936782 0.66522989 0.5387931 0.74547804 0.56589147]
----------------------------------------
Trial 3907
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=109,
random_state=100))])
cv score: [0.61350575 0.59770115 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 3908
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='log2',
n_estimators=27, random_state=100,
subsample=0.95))])
cv score: [0.52586207 0.49281609 0.54022989 0.48062016 0.50387597]
----------------------------------------
Trial 3909
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78376437 0.60775862 0.69396552 0.62661499 0.46640827]
----------------------------------------
Trial 3910
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=45,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.65804598 0.67097701 0.64082687 0.38242894]
----------------------------------------
Trial 3911
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=104,
random_state=100))])
cv score: [0.63218391 0.66810345 0.59626437 0.63824289 0.41472868]
----------------------------------------
Trial 3912
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=34, random_state=100,
subsample=0.75))])
cv score: [0.63793103 0.70689655 0.84770115 0.66925065 0.45348837]
----------------------------------------
Trial 3913
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=154, random_state=100,
subsample=0.6))])
cv score: [0.62356322 0.65804598 0.49712644 0.61369509 0.4754522 ]
----------------------------------------
Trial 3914
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=71, random_state=100,
subsample=0.8))])
cv score: [0.66235632 0.67816092 0.61925287 0.61757106 0.43669251]
----------------------------------------
Trial 3915
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=102,
random_state=100))])
cv score: [0.61350575 0.65373563 0.60775862 0.65503876 0.43281654]
----------------------------------------
Trial 3916
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=91,
random_state=100))])
cv score: [0.55316092 0.67313218 0.63362069 0.72609819 0.3501292 ]
----------------------------------------
Trial 3917
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69827586 0.62931034 0.57902299 0.54780362 0.47028424]
----------------------------------------
Trial 3918
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
n_estimators=164, random_state=100,
subsample=0.7))])
cv score: [0.51867816 0.45258621 0.55028736 0.62661499 0.48320413]
----------------------------------------
Trial 3919
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70474138 0.57183908 0.70402299 0.5503876 0.38565891]
----------------------------------------
Trial 3920
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=35,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73922414 0.56824713 0.60632184 0.52842377 0.4244186 ]
----------------------------------------
Trial 3921
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=187, random_state=100,
subsample=0.6))])
cv score: [0.73850575 0.66666667 0.66810345 0.61886305 0.43152455]
----------------------------------------
Trial 3922
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=76,
random_state=100))])
cv score: [0.61422414 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 3923
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='sqrt',
n_estimators=107, random_state=100,
subsample=0.85))])
cv score: [0.73706897 0.64655172 0.6795977 0.65374677 0.39664083]
----------------------------------------
Trial 3924
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=77,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69683908 0.61206897 0.62931034 0.56847545 0.4379845 ]
----------------------------------------
Trial 3925
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.6954023 0.6091954 0.63824289 0.44186047]
----------------------------------------
Trial 3926
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.64367816 0.69683908 0.6375969 0.42700258]
----------------------------------------
Trial 3927
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=172,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.70833333 0.57471264 0.52713178 0.45090439]
----------------------------------------
Trial 3928
----------------------------------------
Parameters {'rf__n_estimators': 127, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=127,
random_state=100))])
cv score: [0.73132184 0.64511494 0.59195402 0.63049096 0.40568475]
----------------------------------------
Trial 3929
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=11, max_features='log2',
n_estimators=101, random_state=100,
subsample=0.8))])
cv score: [0.62068966 0.60201149 0.47413793 0.62144703 0.45736434]
----------------------------------------
Trial 3930
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=107,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55747126 0.62212644 0.58189655 0.57751938 0.41602067]
----------------------------------------
Trial 3931
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='sqrt',
n_estimators=102, random_state=100,
subsample=0.7))])
cv score: [0.75 0.65229885 0.57471264 0.64728682 0.4121447 ]
----------------------------------------
Trial 3932
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77945402 0.60775862 0.71982759 0.63307494 0.45478036]
----------------------------------------
Trial 3933
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=135,
random_state=100))])
cv score: [0.64008621 0.59770115 0.56178161 0.51744186 0.41860465]
----------------------------------------
Trial 3934
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=41, random_state=100))])
cv score: [0.58692529 0.70545977 0.61422414 0.69702842 0.43281654]
----------------------------------------
Trial 3935
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=105,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7191092 0.68534483 0.70689655 0.68540052 0.38953488]
----------------------------------------
Trial 3936
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=145,
random_state=100))])
cv score: [0.77011494 0.63505747 0.68318966 0.65762274 0.40826873]
----------------------------------------
Trial 3937
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=14, random_state=100,
subsample=0.8))])
cv score: [0.54310345 0.58333333 0.49568966 0.6369509 0.45090439]
----------------------------------------
Trial 3938
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=68,
random_state=100))])
cv score: [0.60344828 0.62571839 0.61350575 0.69250646 0.38113695]
----------------------------------------
Trial 3939
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=95,
random_state=100))])
cv score: [0.54741379 0.69396552 0.42097701 0.59819121 0.36369509]
----------------------------------------
Trial 3940
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=65, random_state=100,
subsample=0.95))])
cv score: [0.70977011 0.6408046 0.5862069 0.60981912 0.40310078]
----------------------------------------
Trial 3941
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=189, random_state=100))])
cv score: [0.60057471 0.73922414 0.64942529 0.67958656 0.43927649]
----------------------------------------
Trial 3942
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=33,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.68965517 0.52011494 0.65633075 0.4121447 ]
----------------------------------------
Trial 3943
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55890805 0.56321839 0.63074713 0.59431525 0.40439276]
----------------------------------------
Trial 3944
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=142,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75431034 0.63793103 0.68965517 0.62015504 0.47416021]
----------------------------------------
Trial 3945
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=136,
random_state=100))])
cv score: [0.61278736 0.60201149 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 3946
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71408046 0.57614943 0.64511494 0.55813953 0.39341085]
----------------------------------------
Trial 3947
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=171,
random_state=100))])
cv score: [0.69827586 0.65373563 0.66810345 0.65245478 0.42764858]
----------------------------------------
Trial 3948
----------------------------------------
Parameters {'xgb__n_estimators': 169, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=169,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.72557471 0.53017241 0.59689922 0.40826873]
----------------------------------------
Trial 3949
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
max_features='sqrt',
n_estimators=130, random_state=100,
subsample=0.65))])
cv score: [0.72701149 0.61781609 0.67241379 0.61369509 0.42635659]
----------------------------------------
Trial 3950
----------------------------------------
Parameters {'xgb__n_estimators': 19, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=19,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.51867816 0.66666667 0.67241379 0.72351421 0.49224806]
----------------------------------------
Trial 3951
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=125,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55172414 0.70833333 0.59913793 0.60852713 0.38501292]
----------------------------------------
Trial 3952
----------------------------------------
Parameters {'xgb__n_estimators': 142, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=142,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80100575 0.64367816 0.67385057 0.60271318 0.40956072]
----------------------------------------
Trial 3953
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=168, random_state=100))])
cv score: [0.74712644 0.62212644 0.67385057 0.65116279 0.40956072]
----------------------------------------
Trial 3954
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72988506 0.5862069 0.68247126 0.65891473 0.42248062]
----------------------------------------
Trial 3955
----------------------------------------
Parameters {'rf__n_estimators': 79, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=79, random_state=100))])
cv score: [0.68678161 0.68247126 0.59913793 0.625323 0.42248062]
----------------------------------------
Trial 3956
----------------------------------------
Parameters {'rf__n_estimators': 109, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=109, random_state=100))])
cv score: [0.54885057 0.66882184 0.60488506 0.59302326 0.40374677]
----------------------------------------
Trial 3957
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60632184 0.74137931 0.59913793 0.60077519 0.44315245]
----------------------------------------
Trial 3958
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=117,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.62643678 0.61781609 0.63824289 0.38501292]
----------------------------------------
Trial 3959
----------------------------------------
Parameters {'gb__n_estimators': 59, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='log2',
n_estimators=59,
random_state=100))])
cv score: [0.58908046 0.70545977 0.60488506 0.7248062 0.47803618]
----------------------------------------
Trial 3960
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=163,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.70114943 0.57327586 0.55684755 0.43281654]
----------------------------------------
Trial 3961
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=34, random_state=100))])
cv score: [0.7341954 0.66522989 0.5862069 0.59689922 0.51421189]
----------------------------------------
Trial 3962
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=30,
random_state=100))])
cv score: [0.63864943 0.60488506 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 3963
----------------------------------------
Parameters {'rf__n_estimators': 105, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=105,
random_state=100))])
cv score: [0.71264368 0.64798851 0.66954023 0.65633075 0.40439276]
----------------------------------------
Trial 3964
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='log2',
n_estimators=11, random_state=100,
subsample=0.95))])
cv score: [0.62068966 0.66091954 0.60201149 0.6744186 0.40374677]
----------------------------------------
Trial 3965
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=57, random_state=100))])
cv score: [0.71264368 0.66954023 0.56178161 0.54909561 0.34754522]
----------------------------------------
Trial 3966
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=164, random_state=100))])
cv score: [0.71982759 0.65229885 0.65373563 0.66795866 0.43152455]
----------------------------------------
Trial 3967
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=191,
random_state=100))])
cv score: [0.57327586 0.59482759 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 3968
----------------------------------------
Parameters {'xgb__n_estimators': 141, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=141,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.63218391 0.57327586 0.65891473 0.4250646 ]
----------------------------------------
Trial 3969
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=157, random_state=100,
subsample=0.65))])
cv score: [0.55890805 0.6954023 0.45689655 0.62144703 0.50258398]
----------------------------------------
Trial 3970
----------------------------------------
Parameters {'rf__n_estimators': 142, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=142, random_state=100))])
cv score: [0.72988506 0.63362069 0.66235632 0.63824289 0.40697674]
----------------------------------------
Trial 3971
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=196, random_state=100,
subsample=0.65))])
cv score: [0.60344828 0.66810345 0.56034483 0.62919897 0.48449612]
----------------------------------------
Trial 3972
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73994253 0.65948276 0.68821839 0.63307494 0.42894057]
----------------------------------------
Trial 3973
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=70, random_state=100,
subsample=0.9))])
cv score: [0.61063218 0.7341954 0.58764368 0.61757106 0.44573643]
----------------------------------------
Trial 3974
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=150, random_state=100,
subsample=0.8))])
cv score: [0.7183908 0.67241379 0.61494253 0.67958656 0.43540052]
----------------------------------------
Trial 3975
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=177, random_state=100))])
cv score: [0.60344828 0.67241379 0.58045977 0.62790698 0.43281654]
----------------------------------------
Trial 3976
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=171, random_state=100))])
cv score: [0.60775862 0.70402299 0.66666667 0.68475452 0.44315245]
----------------------------------------
Trial 3977
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='log2',
n_estimators=105, random_state=100,
subsample=0.7))])
cv score: [0.60344828 0.66954023 0.44827586 0.61886305 0.43152455]
----------------------------------------
Trial 3978
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=176,
random_state=100))])
cv score: [0.70114943 0.65373563 0.66810345 0.65633075 0.42764858]
----------------------------------------
Trial 3979
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=160, random_state=100,
subsample=0.8))])
cv score: [0.64367816 0.72413793 0.52729885 0.58268734 0.49354005]
----------------------------------------
Trial 3980
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=121,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.70689655 0.53735632 0.55297158 0.36563307]
----------------------------------------
Trial 3981
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=114,
random_state=100))])
cv score: [0.72701149 0.64655172 0.6091954 0.61111111 0.39276486]
----------------------------------------
Trial 3982
----------------------------------------
Parameters {'gb__n_estimators': 61, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='sqrt',
n_estimators=61, random_state=100,
subsample=0.85))])
cv score: [0.75862069 0.64798851 0.64511494 0.5878553 0.40180879]
----------------------------------------
Trial 3983
----------------------------------------
Parameters {'gb__n_estimators': 106, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='log2',
n_estimators=106, random_state=100,
subsample=0.7))])
cv score: [0.52011494 0.67816092 0.53591954 0.62273902 0.49095607]
----------------------------------------
Trial 3984
----------------------------------------
Parameters {'gb__n_estimators': 184, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=184,
random_state=100))])
cv score: [0.50718391 0.61206897 0.42241379 0.65633075 0.47416021]
----------------------------------------
Trial 3985
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, max_features='sqrt',
n_estimators=127, random_state=100,
subsample=0.95))])
cv score: [0.61781609 0.68534483 0.49425287 0.59431525 0.49612403]
----------------------------------------
Trial 3986
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='log2',
n_estimators=186, random_state=100,
subsample=0.75))])
cv score: [0.57183908 0.66810345 0.60632184 0.65891473 0.41343669]
----------------------------------------
Trial 3987
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='sqrt',
n_estimators=160, random_state=100,
subsample=0.95))])
cv score: [0.55316092 0.66235632 0.40373563 0.60465116 0.4250646 ]
----------------------------------------
Trial 3988
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=48, random_state=100))])
cv score: [0.5237069 0.64295977 0.56968391 0.55232558 0.43410853]
----------------------------------------
Trial 3989
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=75,
random_state=100))])
cv score: [0.71408046 0.64367816 0.65373563 0.5994832 0.37855297]
----------------------------------------
Trial 3990
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=14, random_state=100))])
cv score: [0.51077586 0.62428161 0.58764368 0.57235142 0.53165375]
----------------------------------------
Trial 3991
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.71551724 0.60201149 0.62144703 0.44832041]
----------------------------------------
Trial 3992
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=78, random_state=100))])
cv score: [0.68821839 0.68247126 0.60201149 0.62661499 0.41731266]
----------------------------------------
Trial 3993
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=182, random_state=100,
subsample=0.75))])
cv score: [0.65229885 0.68534483 0.58477011 0.64470284 0.48449612]
----------------------------------------
Trial 3994
----------------------------------------
Parameters {'xgb__n_estimators': 26, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=26,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.65229885 0.61278736 0.65762274 0.45607235]
----------------------------------------
Trial 3995
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=73,
random_state=100))])
cv score: [0.50862069 0.60344828 0.65229885 0.66537468 0.35465116]
----------------------------------------
Trial 3996
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.70833333 0.62787356 0.61757106 0.44832041]
----------------------------------------
Trial 3997
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
n_estimators=160, random_state=100,
subsample=0.6))])
cv score: [0.5933908 0.7112069 0.59770115 0.66020672 0.44832041]
----------------------------------------
Trial 3998
----------------------------------------
Parameters {'xgb__n_estimators': 85, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=85,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73275862 0.65517241 0.67816092 0.65826873 0.44379845]
----------------------------------------
Trial 3999
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=163,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.69683908 0.60344828 0.56330749 0.50387597]
----------------------------------------
Trial 4000
----------------------------------------
Parameters {'xgb__n_estimators': 191, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=191,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.70833333 0.56321839 0.53617571 0.41343669]
----------------------------------------
Trial 4001
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66235632 0.68103448 0.66810345 0.63824289 0.41472868]
----------------------------------------
Trial 4002
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=153, random_state=100))])
cv score: [0.7183908 0.62643678 0.65373563 0.60852713 0.41602067]
----------------------------------------
Trial 4003
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=72,
random_state=100))])
cv score: [0.5933908 0.63721264 0.625 0.69121447 0.38113695]
----------------------------------------
Trial 4004
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=59,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80890805 0.57255747 0.66163793 0.61692506 0.4625323 ]
----------------------------------------
Trial 4005
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=129, random_state=100,
subsample=0.8))])
cv score: [0.6795977 0.70258621 0.70689655 0.62144703 0.44444444]
----------------------------------------
Trial 4006
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.73563218 0.60775862 0.66666667 0.44056848]
----------------------------------------
Trial 4007
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=168, random_state=100,
subsample=0.8))])
cv score: [0.61925287 0.66954023 0.66954023 0.61627907 0.56459948]
----------------------------------------
Trial 4008
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=93,
random_state=100))])
cv score: [0.72270115 0.64367816 0.63936782 0.61498708 0.38113695]
----------------------------------------
Trial 4009
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56178161 0.7183908 0.57902299 0.56072351 0.48966408]
----------------------------------------
Trial 4010
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='log2',
n_estimators=81, random_state=100,
subsample=0.95))])
cv score: [0.58189655 0.66091954 0.58764368 0.66020672 0.47286822]
----------------------------------------
Trial 4011
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='log2',
n_estimators=124,
random_state=100))])
cv score: [0.76293103 0.63793103 0.72557471 0.65633075 0.38307494]
----------------------------------------
Trial 4012
----------------------------------------
Parameters {'rf__n_estimators': 148, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=148,
random_state=100))])
cv score: [0.61422414 0.60201149 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 4013
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='sqrt',
n_estimators=32, random_state=100,
subsample=0.7))])
cv score: [0.65948276 0.64798851 0.48275862 0.65891473 0.4379845 ]
----------------------------------------
Trial 4014
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=96,
random_state=100))])
cv score: [0.63074713 0.66666667 0.60488506 0.62919897 0.4005168 ]
----------------------------------------
Trial 4015
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=15,
random_state=100))])
cv score: [0.78017241 0.65229885 0.57543103 0.63113695 0.39793282]
----------------------------------------
Trial 4016
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=62,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55316092 0.68103448 0.70977011 0.60335917 0.41731266]
----------------------------------------
Trial 4017
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=32,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.62356322 0.56034483 0.55426357 0.41472868]
----------------------------------------
Trial 4018
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='log2',
n_estimators=54,
random_state=100))])
cv score: [0.67097701 0.67816092 0.70114943 0.65503876 0.4625323 ]
----------------------------------------
Trial 4019
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=10,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55172414 0.66954023 0.60272989 0.59237726 0.39405685]
----------------------------------------
Trial 4020
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
n_estimators=73, random_state=100,
subsample=0.75))])
cv score: [0.59626437 0.56321839 0.47701149 0.54909561 0.52196382]
----------------------------------------
Trial 4021
----------------------------------------
Parameters {'gb__n_estimators': 31, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=31, random_state=100,
subsample=0.6))])
cv score: [0.75431034 0.63074713 0.54166667 0.67183463 0.40826873]
----------------------------------------
Trial 4022
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=170,
random_state=100))])
cv score: [0.69971264 0.65517241 0.67097701 0.64857881 0.42894057]
----------------------------------------
Trial 4023
----------------------------------------
Parameters {'rf__n_estimators': 49, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=49, random_state=100))])
cv score: [0.72844828 0.68534483 0.65086207 0.64082687 0.48578811]
----------------------------------------
Trial 4024
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=60, random_state=100,
subsample=0.7))])
cv score: [0.61063218 0.6566092 0.48850575 0.63953488 0.43540052]
----------------------------------------
Trial 4025
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=12,
random_state=100))])
cv score: [0.49568966 0.79956897 0.53448276 0.54392765 0.4870801 ]
----------------------------------------
Trial 4026
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=103,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70114943 0.71408046 0.63793103 0.61369509 0.43410853]
----------------------------------------
Trial 4027
----------------------------------------
Parameters {'xgb__n_estimators': 116, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=116,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55028736 0.58908046 0.61063218 0.53617571 0.46640827]
----------------------------------------
Trial 4028
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=93,
random_state=100))])
cv score: [0.56321839 0.61063218 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 4029
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=135, random_state=100))])
cv score: [0.5862069 0.65517241 0.60488506 0.64728682 0.44056848]
----------------------------------------
Trial 4030
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=102, random_state=100))])
cv score: [0.75718391 0.63793103 0.64511494 0.625323 0.39534884]
----------------------------------------
Trial 4031
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56752874 0.66954023 0.58477011 0.5503876 0.41472868]
----------------------------------------
Trial 4032
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=167, random_state=100))])
cv score: [0.67385057 0.66954023 0.63936782 0.68346253 0.45478036]
----------------------------------------
Trial 4033
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=77,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.68390805 0.70545977 0.63049096 0.41989664]
----------------------------------------
Trial 4034
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=194, random_state=100,
subsample=0.75))])
cv score: [0.58908046 0.65229885 0.59482759 0.64857881 0.4496124 ]
----------------------------------------
Trial 4035
----------------------------------------
Parameters {'xgb__n_estimators': 101, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=101,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62068966 0.67816092 0.61637931 0.6369509 0.40568475]
----------------------------------------
Trial 4036
----------------------------------------
Parameters {'xgb__n_estimators': 13, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=13,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72270115 0.60201149 0.6716954 0.55426357 0.44444444]
----------------------------------------
Trial 4037
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='sqrt',
n_estimators=197, random_state=100,
subsample=0.9))])
cv score: [0.75862069 0.63362069 0.70402299 0.64276486 0.3630491 ]
----------------------------------------
Trial 4038
----------------------------------------
Parameters {'gb__n_estimators': 107, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='sqrt',
n_estimators=107, random_state=100,
subsample=0.75))])
cv score: [0.61063218 0.70833333 0.55747126 0.59819121 0.46899225]
----------------------------------------
Trial 4039
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
max_features='log2',
n_estimators=169, random_state=100,
subsample=0.65))])
cv score: [0.61063218 0.67097701 0.4137931 0.48901809 0.48191214]
----------------------------------------
Trial 4040
----------------------------------------
Parameters {'xgb__n_estimators': 22, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=22,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5933908 0.57183908 0.51005747 0.66020672 0.36563307]
----------------------------------------
Trial 4041
----------------------------------------
Parameters {'gb__n_estimators': 91, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=91, random_state=100,
subsample=0.8))])
cv score: [0.59770115 0.69827586 0.56178161 0.64599483 0.53100775]
----------------------------------------
Trial 4042
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
max_features='log2',
n_estimators=105, random_state=100,
subsample=0.8))])
cv score: [0.55890805 0.57614943 0.61494253 0.70284238 0.35917313]
----------------------------------------
Trial 4043
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=72,
random_state=100))])
cv score: [0.71264368 0.62356322 0.67816092 0.60465116 0.37209302]
----------------------------------------
Trial 4044
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74137931 0.66522989 0.68534483 0.61757106 0.45542636]
----------------------------------------
Trial 4045
----------------------------------------
Parameters {'rf__n_estimators': 126, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=126,
random_state=100))])
cv score: [0.63793103 0.66666667 0.58908046 0.63565891 0.4379845 ]
----------------------------------------
Trial 4046
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.61637931 0.4841954 0.6369509 0.39147287]
----------------------------------------
Trial 4047
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=114,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70833333 0.63074713 0.66091954 0.6124031 0.43023256]
----------------------------------------
Trial 4048
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, n_estimators=155,
random_state=100,
subsample=0.85))])
cv score: [0.64367816 0.69683908 0.57327586 0.62403101 0.4121447 ]
----------------------------------------
Trial 4049
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='sqrt',
n_estimators=169, random_state=100,
subsample=0.65))])
cv score: [0.6091954 0.68247126 0.62356322 0.67829457 0.4496124 ]
----------------------------------------
Trial 4050
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=81,
random_state=100,
subsample=0.95))])
cv score: [0.51149425 0.75 0.61206897 0.63824289 0.43023256]
----------------------------------------
Trial 4051
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.6566092 0.65804598 0.54909561 0.42248062]
----------------------------------------
Trial 4052
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.72413793 0.61206897 0.62403101 0.42377261]
----------------------------------------
Trial 4053
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
n_estimators=108, random_state=100,
subsample=0.8))])
cv score: [0.5545977 0.72126437 0.55603448 0.60077519 0.45607235]
----------------------------------------
Trial 4054
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='sqrt',
n_estimators=179, random_state=100,
subsample=0.95))])
cv score: [0.64655172 0.66810345 0.47557471 0.66925065 0.49870801]
----------------------------------------
Trial 4055
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.68678161 0.63362069 0.625323 0.40180879]
----------------------------------------
Trial 4056
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7033046 0.70402299 0.63793103 0.58462532 0.38049096]
----------------------------------------
Trial 4057
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=153,
random_state=100))])
cv score: [0.66091954 0.63793103 0.63505747 0.63824289 0.45090439]
----------------------------------------
Trial 4058
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=17,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71551724 0.60057471 0.51293103 0.56136951 0.39534884]
----------------------------------------
Trial 4059
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=117,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56609195 0.65948276 0.52155172 0.54134367 0.28682171]
----------------------------------------
Trial 4060
----------------------------------------
Parameters {'gb__n_estimators': 137, 'gb__subsample': 0.6, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='sqrt',
n_estimators=137, random_state=100,
subsample=0.6))])
cv score: [0.69109195 0.66810345 0.59195402 0.60465116 0.42894057]
----------------------------------------
Trial 4061
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
max_features='log2',
n_estimators=39, random_state=100,
subsample=0.65))])
cv score: [0.63505747 0.66666667 0.58189655 0.64857881 0.37984496]
----------------------------------------
Trial 4062
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=10, random_state=100,
subsample=0.7))])
cv score: [0.60344828 0.66522989 0.6408046 0.74289406 0.42958656]
----------------------------------------
Trial 4063
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='sqrt',
n_estimators=173, random_state=100,
subsample=0.95))])
cv score: [0.62931034 0.6954023 0.54597701 0.60723514 0.48062016]
----------------------------------------
Trial 4064
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.65948276 0.62068966 0.60594315 0.43410853]
----------------------------------------
Trial 4065
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='log2',
n_estimators=141, random_state=100,
subsample=0.95))])
cv score: [0.53448276 0.72126437 0.63362069 0.63565891 0.41731266]
----------------------------------------
Trial 4066
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.66810345 0.55316092 0.55684755 0.42118863]
----------------------------------------
Trial 4067
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=72,
random_state=100, subsample=0.7))])
cv score: [0.56465517 0.72413793 0.5316092 0.6744186 0.4625323 ]
----------------------------------------
Trial 4068
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=84,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66666667 0.68678161 0.65517241 0.60335917 0.44315245]
----------------------------------------
Trial 4069
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=45,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.66954023 0.68390805 0.60981912 0.45478036]
----------------------------------------
Trial 4070
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 1.0, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=38,
random_state=100))])
cv score: [0.54454023 0.75862069 0.46982759 0.72222222 0.35271318]
----------------------------------------
Trial 4071
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=80,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.65229885 0.68390805 0.60206718 0.45090439]
----------------------------------------
Trial 4072
----------------------------------------
Parameters {'gb__n_estimators': 56, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='sqrt',
n_estimators=56, random_state=100,
subsample=0.6))])
cv score: [0.54885057 0.6795977 0.51293103 0.56072351 0.3875969 ]
----------------------------------------
Trial 4073
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=185,
random_state=100))])
cv score: [0.75862069 0.64942529 0.68821839 0.64470284 0.44056848]
----------------------------------------
Trial 4074
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=189, random_state=100))])
cv score: [0.58979885 0.6875 0.58477011 0.63113695 0.4379845 ]
----------------------------------------
Trial 4075
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.73994253 0.61350575 0.61886305 0.37080103]
----------------------------------------
Trial 4076
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=74,
random_state=100))])
cv score: [0.59123563 0.65948276 0.66091954 0.71640827 0.38824289]
----------------------------------------
Trial 4077
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=157, random_state=100,
subsample=0.7))])
cv score: [0.52729885 0.73275862 0.54741379 0.69121447 0.44444444]
----------------------------------------
Trial 4078
----------------------------------------
Parameters {'xgb__n_estimators': 102, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=102,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70617816 0.58189655 0.69109195 0.54651163 0.38113695]
----------------------------------------
Trial 4079
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=117,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.6566092 0.49712644 0.45994832 0.47803618]
----------------------------------------
Trial 4080
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='sqrt',
n_estimators=13, random_state=100))])
cv score: [0.67528736 0.62787356 0.74856322 0.51744186 0.48514212]
----------------------------------------
Trial 4081
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=150,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.68678161 0.61206897 0.66020672 0.39922481]
----------------------------------------
Trial 4082
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70617816 0.58045977 0.60057471 0.56589147 0.40826873]
----------------------------------------
Trial 4083
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='sqrt',
n_estimators=87, random_state=100,
subsample=0.8))])
cv score: [0.57902299 0.61350575 0.54166667 0.60465116 0.4379845 ]
----------------------------------------
Trial 4084
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56609195 0.69109195 0.64942529 0.58139535 0.39018088]
----------------------------------------
Trial 4085
----------------------------------------
Parameters {'gb__n_estimators': 114, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
n_estimators=114, random_state=100,
subsample=0.6))])
cv score: [0.77873563 0.65014368 0.60775862 0.64341085 0.40697674]
----------------------------------------
Trial 4086
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=43, random_state=100,
subsample=0.6))])
cv score: [0.74281609 0.68103448 0.77586207 0.65503876 0.43217054]
----------------------------------------
Trial 4087
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
max_features='sqrt',
n_estimators=39,
random_state=100))])
cv score: [0.76293103 0.68678161 0.73850575 0.63242894 0.36950904]
----------------------------------------
Trial 4088
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69252874 0.65086207 0.66522989 0.60206718 0.46770026]
----------------------------------------
Trial 4089
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.74137931 0.6091954 0.63178295 0.44315245]
----------------------------------------
Trial 4090
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=77,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5862069 0.67816092 0.67816092 0.66149871 0.38888889]
----------------------------------------
Trial 4091
----------------------------------------
Parameters {'xgb__n_estimators': 27, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=27,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76724138 0.60272989 0.72341954 0.63242894 0.36498708]
----------------------------------------
Trial 4092
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='sqrt',
n_estimators=139,
random_state=100))])
cv score: [0.60488506 0.6795977 0.57327586 0.6744186 0.48191214]
----------------------------------------
Trial 4093
----------------------------------------
Parameters {'gb__n_estimators': 21, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='sqrt',
n_estimators=21,
random_state=100))])
cv score: [0.62212644 0.63505747 0.62643678 0.72997416 0.47028424]
----------------------------------------
Trial 4094
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=159,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76293103 0.62212644 0.67528736 0.65633075 0.37596899]
----------------------------------------
Trial 4095
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63362069 0.61637931 0.68103448 0.55943152 0.46511628]
----------------------------------------
Trial 4096
----------------------------------------
Parameters {'gb__n_estimators': 139, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3,
max_features='sqrt',
n_estimators=139, random_state=100,
subsample=0.9))])
cv score: [0.61781609 0.67816092 0.55028736 0.60335917 0.55426357]
----------------------------------------
Trial 4097
----------------------------------------
Parameters {'gb__n_estimators': 89, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
max_features='log2',
n_estimators=89, random_state=100,
subsample=0.65))])
cv score: [0.61925287 0.71551724 0.46408046 0.64599483 0.48191214]
----------------------------------------
Trial 4098
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=171, random_state=100))])
cv score: [0.5704023 0.64224138 0.57614943 0.64082687 0.42377261]
----------------------------------------
Trial 4099
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=149,
random_state=100))])
cv score: [0.64942529 0.6954023 0.59913793 0.60206718 0.47028424]
----------------------------------------
Trial 4100
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=101, random_state=100))])
cv score: [0.63505747 0.6637931 0.51867816 0.66925065 0.37984496]
----------------------------------------
Trial 4101
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=55,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75143678 0.66522989 0.68965517 0.60077519 0.34625323]
----------------------------------------
Trial 4102
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64511494 0.70043103 0.76724138 0.60335917 0.36821705]
----------------------------------------
Trial 4103
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=137,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.73563218 0.59051724 0.58139535 0.42377261]
----------------------------------------
Trial 4104
----------------------------------------
Parameters {'xgb__n_estimators': 139, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=139,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.69109195 0.63218391 0.59560724 0.42894057]
----------------------------------------
Trial 4105
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=71,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5545977 0.63505747 0.49568966 0.6498708 0.46511628]
----------------------------------------
Trial 4106
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75502874 0.59841954 0.67887931 0.60788114 0.41925065]
----------------------------------------
Trial 4107
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=127,
random_state=100))])
cv score: [0.52729885 0.66954023 0.46982759 0.69379845 0.40180879]
----------------------------------------
Trial 4108
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=186,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74281609 0.61278736 0.63362069 0.60981912 0.44315245]
----------------------------------------
Trial 4109
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
max_features='sqrt',
n_estimators=174, random_state=100,
subsample=0.7))])
cv score: [0.57902299 0.61494253 0.53591954 0.62273902 0.4625323 ]
----------------------------------------
Trial 4110
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=59,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68821839 0.6408046 0.66522989 0.65762274 0.40826873]
----------------------------------------
Trial 4111
----------------------------------------
Parameters {'gb__n_estimators': 186, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='sqrt',
n_estimators=186, random_state=100,
subsample=0.85))])
cv score: [0.69252874 0.69252874 0.63793103 0.6498708 0.47803618]
----------------------------------------
Trial 4112
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=88,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73706897 0.66954023 0.66091954 0.61886305 0.45607235]
----------------------------------------
Trial 4113
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68390805 0.67241379 0.66091954 0.61627907 0.45090439]
----------------------------------------
Trial 4114
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=170, random_state=100,
subsample=0.75))])
cv score: [0.56034483 0.54022989 0.41091954 0.36563307 0.53100775]
----------------------------------------
Trial 4115
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.71982759 0.70689655 0.63565891 0.39276486]
----------------------------------------
Trial 4116
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=68, random_state=100))])
cv score: [0.59482759 0.70761494 0.64798851 0.66020672 0.4379845 ]
----------------------------------------
Trial 4117
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.65, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=156, random_state=100,
subsample=0.65))])
cv score: [0.62356322 0.7341954 0.49281609 0.5878553 0.49354005]
----------------------------------------
Trial 4118
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.70977011 0.60057471 0.625323 0.40180879]
----------------------------------------
Trial 4119
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=74, random_state=100))])
cv score: [0.60201149 0.65229885 0.53520115 0.59689922 0.43023256]
----------------------------------------
Trial 4120
----------------------------------------
Parameters {'gb__n_estimators': 199, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
max_features='log2',
n_estimators=199, random_state=100,
subsample=0.95))])
cv score: [0.75718391 0.61781609 0.72844828 0.63178295 0.37403101]
----------------------------------------
Trial 4121
----------------------------------------
Parameters {'gb__n_estimators': 174, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
n_estimators=174, random_state=100,
subsample=0.85))])
cv score: [0.59195402 0.72844828 0.61637931 0.72351421 0.42377261]
----------------------------------------
Trial 4122
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=47, random_state=100,
subsample=0.9))])
cv score: [0.29238506 0.57471264 0.41163793 0.56459948 0.54521964]
----------------------------------------
Trial 4123
----------------------------------------
Parameters {'gb__n_estimators': 188, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='log2',
n_estimators=188, random_state=100,
subsample=0.65))])
cv score: [0.54022989 0.44683908 0.47988506 0.50516796 0.34496124]
----------------------------------------
Trial 4124
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=85,
random_state=100))])
cv score: [0.59698276 0.66594828 0.65804598 0.70284238 0.35917313]
----------------------------------------
Trial 4125
----------------------------------------
Parameters {'rf__n_estimators': 175, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=175,
random_state=100))])
cv score: [0.77298851 0.63362069 0.69109195 0.64082687 0.39534884]
----------------------------------------
Trial 4126
----------------------------------------
Parameters {'xgb__n_estimators': 110, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=110,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.72270115 0.60775862 0.62144703 0.38888889]
----------------------------------------
Trial 4127
----------------------------------------
Parameters {'rf__n_estimators': 184, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=184, random_state=100))])
cv score: [0.76867816 0.6408046 0.56034483 0.62661499 0.40762274]
----------------------------------------
Trial 4128
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=105,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.70402299 0.72988506 0.65245478 0.37984496]
----------------------------------------
Trial 4129
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
max_features='log2',
n_estimators=25, random_state=100,
subsample=0.8))])
cv score: [0.66810345 0.6566092 0.47844828 0.54392765 0.49483204]
----------------------------------------
Trial 4130
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=132,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.67097701 0.67816092 0.6498708 0.39534884]
----------------------------------------
Trial 4131
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=66, random_state=100))])
cv score: [0.54022989 0.62859195 0.55890805 0.59431525 0.46382429]
----------------------------------------
Trial 4132
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=37,
random_state=100))])
cv score: [0.56321839 0.6091954 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 4133
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=151,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.76867816 0.61350575 0.5878553 0.40826873]
----------------------------------------
Trial 4134
----------------------------------------
Parameters {'gb__n_estimators': 23, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
n_estimators=23, random_state=100,
subsample=0.65))])
cv score: [0.38505747 0.4841954 0.47126437 0.67958656 0.50904393]
----------------------------------------
Trial 4135
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=89, random_state=100))])
cv score: [0.61206897 0.62787356 0.60488506 0.6873385 0.36692506]
----------------------------------------
Trial 4136
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='sqrt',
n_estimators=108, random_state=100,
subsample=0.7))])
cv score: [0.45833333 0.67025862 0.32399425 0.69896641 0.54263566]
----------------------------------------
Trial 4137
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79597701 0.58764368 0.6795977 0.61498708 0.44186047]
----------------------------------------
Trial 4138
----------------------------------------
Parameters {'rf__n_estimators': 163, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=163,
random_state=100))])
cv score: [0.61063218 0.66666667 0.61278736 0.72351421 0.40826873]
----------------------------------------
Trial 4139
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
n_estimators=69, random_state=100,
subsample=0.75))])
cv score: [0.36494253 0.57183908 0.52442529 0.6744186 0.52196382]
----------------------------------------
Trial 4140
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=58,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.68965517 0.62643678 0.50387597 0.46382429]
----------------------------------------
Trial 4141
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=50,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.64367816 0.55603448 0.58656331 0.39276486]
----------------------------------------
Trial 4142
----------------------------------------
Parameters {'rf__n_estimators': 60, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=60, random_state=100))])
cv score: [0.74425287 0.6566092 0.65086207 0.63178295 0.47028424]
----------------------------------------
Trial 4143
----------------------------------------
Parameters {'gb__n_estimators': 81, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=81, random_state=100,
subsample=0.85))])
cv score: [0.6566092 0.63505747 0.42385057 0.59173127 0.52842377]
----------------------------------------
Trial 4144
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=191,
random_state=100))])
cv score: [0.63721264 0.60057471 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 4145
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.72844828 0.64798851 0.66149871 0.43023256]
----------------------------------------
Trial 4146
----------------------------------------
Parameters {'xgb__n_estimators': 131, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=131,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78951149 0.57471264 0.71048851 0.60594315 0.41795866]
----------------------------------------
Trial 4147
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=29, random_state=100))])
cv score: [0.60775862 0.70258621 0.56896552 0.59625323 0.52390181]
----------------------------------------
Trial 4148
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
n_estimators=71, random_state=100,
subsample=0.7))])
cv score: [0.61637931 0.5466954 0.43534483 0.72351421 0.68217054]
----------------------------------------
Trial 4149
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=63, random_state=100))])
cv score: [0.55244253 0.71695402 0.63649425 0.66731266 0.45219638]
----------------------------------------
Trial 4150
----------------------------------------
Parameters {'rf__n_estimators': 178, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=178,
random_state=100))])
cv score: [0.58764368 0.67528736 0.61637931 0.6744186 0.42118863]
----------------------------------------
Trial 4151
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=90,
random_state=100))])
cv score: [0.61494253 0.65086207 0.58045977 0.6627907 0.41343669]
----------------------------------------
Trial 4152
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.75 0.57902299 0.54005168 0.44379845]
----------------------------------------
Trial 4153
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
max_features='sqrt',
n_estimators=148,
random_state=100))])
cv score: [0.54310345 0.70402299 0.58908046 0.70413437 0.47286822]
----------------------------------------
Trial 4154
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=28,
random_state=100))])
cv score: [0.57758621 0.61135057 0.57758621 0.66795866 0.36563307]
----------------------------------------
Trial 4155
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=77, random_state=100,
subsample=0.65))])
cv score: [0.7341954 0.66451149 0.49137931 0.64470284 0.39341085]
----------------------------------------
Trial 4156
----------------------------------------
Parameters {'rf__n_estimators': 40, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=40,
random_state=100))])
cv score: [0.59770115 0.6566092 0.62643678 0.64599483 0.38888889]
----------------------------------------
Trial 4157
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=51,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77945402 0.61063218 0.64583333 0.6124031 0.44121447]
----------------------------------------
Trial 4158
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=15, random_state=100))])
cv score: [0.7112069 0.63505747 0.33117816 0.60788114 0.39211886]
----------------------------------------
Trial 4159
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=63, random_state=100))])
cv score: [0.5704023 0.70689655 0.6329023 0.6873385 0.4373385 ]
----------------------------------------
Trial 4160
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=128,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 4161
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=119, random_state=100,
subsample=0.8))])
cv score: [0.7112069 0.66666667 0.68318966 0.67118863 0.37790698]
----------------------------------------
Trial 4162
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=131,
random_state=100))])
cv score: [0.74425287 0.64798851 0.69971264 0.64341085 0.43927649]
----------------------------------------
Trial 4163
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=38, random_state=100))])
cv score: [0.56178161 0.70545977 0.64439655 0.59689922 0.46834625]
----------------------------------------
Trial 4164
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=171,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 4165
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=64,
random_state=100))])
cv score: [0.57974138 0.66163793 0.66954023 0.70801034 0.38049096]
----------------------------------------
Trial 4166
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64655172 0.6637931 0.50862069 0.60594315 0.44186047]
----------------------------------------
Trial 4167
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
max_features='sqrt',
n_estimators=49, random_state=100,
subsample=0.95))])
cv score: [0.67385057 0.74856322 0.46982759 0.34883721 0.47157623]
----------------------------------------
Trial 4168
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=44,
random_state=100))])
cv score: [0.58333333 0.64583333 0.65445402 0.66020672 0.43281654]
----------------------------------------
Trial 4169
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=15,
random_state=100))])
cv score: [0.76149425 0.6795977 0.58405172 0.74031008 0.4502584 ]
----------------------------------------
Trial 4170
----------------------------------------
Parameters {'gb__n_estimators': 176, 'gb__subsample': 0.6, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=176, random_state=100,
subsample=0.6))])
cv score: [0.40229885 0.52586207 0.59626437 0.76098191 0.42894057]
----------------------------------------
Trial 4171
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=33, random_state=100))])
cv score: [0.74568966 0.64008621 0.55747126 0.60077519 0.37790698]
----------------------------------------
Trial 4172
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
n_estimators=150, random_state=100,
subsample=0.8))])
cv score: [0.74568966 0.6637931 0.65086207 0.66343669 0.38049096]
----------------------------------------
Trial 4173
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=52, random_state=100))])
cv score: [0.54525862 0.63146552 0.58548851 0.52196382 0.43604651]
----------------------------------------
Trial 4174
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=91,
random_state=100))])
cv score: [0.61350575 0.60057471 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 4175
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, n_estimators=17,
random_state=100,
subsample=0.95))])
cv score: [0.5933908 0.74640805 0.5158046 0.70542636 0.42764858]
----------------------------------------
Trial 4176
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=11,
random_state=100))])
cv score: [0.69612069 0.57614943 0.60632184 0.6996124 0.36563307]
----------------------------------------
Trial 4177
----------------------------------------
Parameters {'rf__n_estimators': 187, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=187,
random_state=100))])
cv score: [0.71264368 0.65948276 0.67528736 0.65633075 0.43410853]
----------------------------------------
Trial 4178
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='sqrt',
n_estimators=141, random_state=100,
subsample=0.7))])
cv score: [0.60057471 0.63936782 0.58045977 0.59173127 0.37726098]
----------------------------------------
Trial 4179
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='sqrt',
n_estimators=24, random_state=100,
subsample=0.7))])
cv score: [0.48132184 0.69971264 0.37068966 0.74031008 0.38113695]
----------------------------------------
Trial 4180
----------------------------------------
Parameters {'gb__n_estimators': 15, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=15, random_state=100,
subsample=0.95))])
cv score: [0.68390805 0.66666667 0.66954023 0.71317829 0.50904393]
----------------------------------------
Trial 4181
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=9,
max_features='sqrt',
n_estimators=77, random_state=100,
subsample=0.75))])
cv score: [0.63074713 0.62931034 0.50287356 0.65116279 0.50129199]
----------------------------------------
Trial 4182
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='log2',
n_estimators=171, random_state=100,
subsample=0.9))])
cv score: [0.63649425 0.65804598 0.60775862 0.62661499 0.42118863]
----------------------------------------
Trial 4183
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=113,
random_state=100))])
cv score: [0.60344828 0.65948276 0.61637931 0.65633075 0.43152455]
----------------------------------------
Trial 4184
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=35,
random_state=100))])
cv score: [0.75574713 0.62356322 0.64511494 0.70155039 0.43540052]
----------------------------------------
Trial 4185
----------------------------------------
Parameters {'xgb__n_estimators': 150, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=150,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78520115 0.65517241 0.64008621 0.63436693 0.3869509 ]
----------------------------------------
Trial 4186
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=145, random_state=100))])
cv score: [0.5625 0.68103448 0.61350575 0.5994832 0.4502584 ]
----------------------------------------
Trial 4187
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=83, random_state=100,
subsample=0.7))])
cv score: [0.61494253 0.6566092 0.53448276 0.73126615 0.49354005]
----------------------------------------
Trial 4188
----------------------------------------
Parameters {'rf__n_estimators': 116, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=116,
random_state=100))])
cv score: [0.61422414 0.59770115 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 4189
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=180, random_state=100,
subsample=0.85))])
cv score: [0.66235632 0.69252874 0.60201149 0.60981912 0.43540052]
----------------------------------------
Trial 4190
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=164,
random_state=100))])
cv score: [0.70114943 0.66091954 0.66810345 0.65245478 0.42894057]
----------------------------------------
Trial 4191
----------------------------------------
Parameters {'gb__n_estimators': 182, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='sqrt',
n_estimators=182, random_state=100,
subsample=0.9))])
cv score: [0.62068966 0.66235632 0.51293103 0.64728682 0.42764858]
----------------------------------------
Trial 4192
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56752874 0.68965517 0.61494253 0.60465116 0.46640827]
----------------------------------------
Trial 4193
----------------------------------------
Parameters {'rf__n_estimators': 24, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=24, random_state=100))])
cv score: [0.55387931 0.61350575 0.48563218 0.63242894 0.45219638]
----------------------------------------
Trial 4194
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=47,
random_state=100))])
cv score: [0.5933908 0.63793103 0.60632184 0.67571059 0.39534884]
----------------------------------------
Trial 4195
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53017241 0.6795977 0.61350575 0.59689922 0.41989664]
----------------------------------------
Trial 4196
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=51, random_state=100))])
cv score: [0.66810345 0.61637931 0.48706897 0.61757106 0.41602067]
----------------------------------------
Trial 4197
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56681034 0.66235632 0.73563218 0.54328165 0.38113695]
----------------------------------------
Trial 4198
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=157, random_state=100,
subsample=0.9))])
cv score: [0.57183908 0.69971264 0.49712644 0.60594315 0.40439276]
----------------------------------------
Trial 4199
----------------------------------------
Parameters {'rf__n_estimators': 90, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=90,
random_state=100))])
cv score: [0.61278736 0.6012931 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 4200
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=128, random_state=100))])
cv score: [0.6408046 0.68678161 0.61781609 0.67958656 0.42635659]
----------------------------------------
Trial 4201
----------------------------------------
Parameters {'gb__n_estimators': 24, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='log2',
n_estimators=24, random_state=100,
subsample=0.75))])
cv score: [0.48850575 0.61206897 0.51005747 0.67958656 0.49354005]
----------------------------------------
Trial 4202
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.8, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='log2',
n_estimators=50, random_state=100,
subsample=0.8))])
cv score: [0.57471264 0.6408046 0.56609195 0.70542636 0.45478036]
----------------------------------------
Trial 4203
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=124, random_state=100,
subsample=0.9))])
cv score: [0.55316092 0.7341954 0.55028736 0.54521964 0.45348837]
----------------------------------------
Trial 4204
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=101,
random_state=100))])
cv score: [0.65086207 0.64224138 0.62643678 0.64341085 0.44056848]
----------------------------------------
Trial 4205
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=180,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.64367816 0.53304598 0.53229974 0.44186047]
----------------------------------------
Trial 4206
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='log2',
n_estimators=38, random_state=100,
subsample=0.75))])
cv score: [0.76149425 0.66235632 0.53448276 0.59689922 0.39922481]
----------------------------------------
Trial 4207
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=196, random_state=100,
subsample=0.7))])
cv score: [0.36063218 0.59051724 0.4066092 0.60206718 0.43281654]
----------------------------------------
Trial 4208
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=10, random_state=100))])
cv score: [0.49568966 0.60847701 0.62715517 0.58462532 0.48514212]
----------------------------------------
Trial 4209
----------------------------------------
Parameters {'xgb__n_estimators': 57, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=57,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.81178161 0.63936782 0.73706897 0.63178295 0.47803618]
----------------------------------------
Trial 4210
----------------------------------------
Parameters {'rf__n_estimators': 108, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=108, random_state=100))])
cv score: [0.5862069 0.65948276 0.59626437 0.61369509 0.44056848]
----------------------------------------
Trial 4211
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='sqrt',
n_estimators=195, random_state=100,
subsample=0.9))])
cv score: [0.65373563 0.65517241 0.54022989 0.64082687 0.44832041]
----------------------------------------
Trial 4212
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=28,
random_state=100))])
cv score: [0.61925287 0.60560345 0.55675287 0.51744186 0.46963824]
----------------------------------------
Trial 4213
----------------------------------------
Parameters {'rf__n_estimators': 37, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=37,
random_state=100))])
cv score: [0.58692529 0.60704023 0.51724138 0.52260982 0.46899225]
----------------------------------------
Trial 4214
----------------------------------------
Parameters {'xgb__n_estimators': 45, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=45,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58764368 0.73994253 0.5316092 0.62144703 0.39276486]
----------------------------------------
Trial 4215
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.64367816 0.68534483 0.65116279 0.40116279]
----------------------------------------
Trial 4216
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=114, random_state=100))])
cv score: [0.73132184 0.63793103 0.65517241 0.57364341 0.37855297]
----------------------------------------
Trial 4217
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=158,
random_state=100))])
cv score: [0.61566092 0.59841954 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 4218
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
n_estimators=51, random_state=100,
subsample=0.9))])
cv score: [0.69971264 0.60488506 0.60344828 0.68346253 0.48191214]
----------------------------------------
Trial 4219
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=109,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7737069 0.57902299 0.75574713 0.55620155 0.40245478]
----------------------------------------
Trial 4220
----------------------------------------
Parameters {'xgb__n_estimators': 80, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=80,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61350575 0.64367816 0.54022989 0.49354005 0.45090439]
----------------------------------------
Trial 4221
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=93, random_state=100))])
cv score: [0.74856322 0.625 0.66810345 0.62273902 0.39793282]
----------------------------------------
Trial 4222
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=196, random_state=100))])
cv score: [0.74137931 0.65086207 0.6408046 0.64341085 0.40956072]
----------------------------------------
Trial 4223
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=64,
random_state=100))])
cv score: [0.60344828 0.63577586 0.60775862 0.68863049 0.36434109]
----------------------------------------
Trial 4224
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=113,
random_state=100))])
cv score: [0.59626437 0.6795977 0.58908046 0.6744186 0.43087855]
----------------------------------------
Trial 4225
----------------------------------------
Parameters {'xgb__n_estimators': 63, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=63,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.64367816 0.7183908 0.65503876 0.37726098]
----------------------------------------
Trial 4226
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=199,
random_state=100))])
cv score: [0.71408046 0.66666667 0.67385057 0.64341085 0.4379845 ]
----------------------------------------
Trial 4227
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=167,
random_state=100))])
cv score: [0.77586207 0.63649425 0.68678161 0.64470284 0.39922481]
----------------------------------------
Trial 4228
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=134, random_state=100))])
cv score: [0.73706897 0.63793103 0.66235632 0.63565891 0.41343669]
----------------------------------------
Trial 4229
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='log2',
n_estimators=169, random_state=100))])
cv score: [0.62068966 0.65086207 0.62068966 0.67829457 0.39276486]
----------------------------------------
Trial 4230
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=135, random_state=100))])
cv score: [0.73706897 0.65373563 0.65373563 0.64857881 0.43152455]
----------------------------------------
Trial 4231
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=50, random_state=100))])
cv score: [0.5933908 0.6408046 0.49137931 0.65762274 0.40697674]
----------------------------------------
Trial 4232
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57183908 0.65086207 0.60344828 0.62273902 0.47028424]
----------------------------------------
Trial 4233
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=115, random_state=100))])
cv score: [0.56465517 0.68678161 0.61206897 0.58527132 0.43087855]
----------------------------------------
Trial 4234
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=196,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57327586 0.66954023 0.60201149 0.62403101 0.41731266]
----------------------------------------
Trial 4235
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=73, random_state=100,
subsample=0.85))])
cv score: [0.61350575 0.63362069 0.50431034 0.54134367 0.56072351]
----------------------------------------
Trial 4236
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
max_features='log2',
n_estimators=42, random_state=100,
subsample=0.75))])
cv score: [0.55603448 0.63649425 0.53448276 0.68604651 0.42635659]
----------------------------------------
Trial 4237
----------------------------------------
Parameters {'rf__n_estimators': 48, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=48, random_state=100))])
cv score: [0.65948276 0.62787356 0.47701149 0.64341085 0.42377261]
----------------------------------------
Trial 4238
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75431034 0.66091954 0.70545977 0.62338501 0.4379845 ]
----------------------------------------
Trial 4239
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74928161 0.55603448 0.68318966 0.63501292 0.39534884]
----------------------------------------
Trial 4240
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=49, random_state=100,
subsample=0.9))])
cv score: [0.62356322 0.69827586 0.58189655 0.67054264 0.52583979]
----------------------------------------
Trial 4241
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=38, random_state=100,
subsample=0.7))])
cv score: [0.61637931 0.62787356 0.58908046 0.67700258 0.41602067]
----------------------------------------
Trial 4242
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='log2',
n_estimators=60, random_state=100,
subsample=0.75))])
cv score: [0.62787356 0.71695402 0.54166667 0.68604651 0.45090439]
----------------------------------------
Trial 4243
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=108,
random_state=100,
subsample=0.95))])
cv score: [0.63074713 0.59051724 0.5545977 0.42635659 0.45865633]
----------------------------------------
Trial 4244
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=121, random_state=100,
subsample=0.85))])
cv score: [0.57183908 0.74856322 0.61925287 0.69250646 0.4005168 ]
----------------------------------------
Trial 4245
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=134,
random_state=100))])
cv score: [0.64008621 0.59770115 0.56178161 0.51744186 0.41860465]
----------------------------------------
Trial 4246
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=35,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.625 0.67672414 0.61925287 0.62273902 0.45219638]
----------------------------------------
Trial 4247
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.56824713 0.43821839 0.59108527 0.41472868]
----------------------------------------
Trial 4248
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66235632 0.67385057 0.64224138 0.67248062 0.57816537]
----------------------------------------
Trial 4249
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=75, random_state=100))])
cv score: [0.61925287 0.62356322 0.62931034 0.70413437 0.36821705]
----------------------------------------
Trial 4250
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=131, random_state=100))])
cv score: [0.55603448 0.69037356 0.60057471 0.62144703 0.43217054]
----------------------------------------
Trial 4251
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.6566092 0.59913793 0.55426357 0.50129199]
----------------------------------------
Trial 4252
----------------------------------------
Parameters {'gb__n_estimators': 152, 'gb__subsample': 0.85, 'gb__learning_rate': 1.0, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=11,
max_features='log2',
n_estimators=152, random_state=100,
subsample=0.85))])
cv score: [0.5387931 0.4612069 0.50718391 0.66537468 0.47803618]
----------------------------------------
Trial 4253
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=193,
random_state=100, subsample=0.8))])
cv score: [0.55890805 0.68534483 0.57471264 0.64341085 0.44186047]
----------------------------------------
Trial 4254
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
max_features='sqrt',
n_estimators=136,
random_state=100))])
cv score: [0.51149425 0.63362069 0.38074713 0.61757106 0.47286822]
----------------------------------------
Trial 4255
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
n_estimators=29, random_state=100,
subsample=0.8))])
cv score: [0.56752874 0.7887931 0.55890805 0.70155039 0.35723514]
----------------------------------------
Trial 4256
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
n_estimators=44, random_state=100,
subsample=0.85))])
cv score: [0.625 0.75431034 0.6091954 0.70155039 0.43087855]
----------------------------------------
Trial 4257
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=154,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77658046 0.61494253 0.68821839 0.59883721 0.4121447 ]
----------------------------------------
Trial 4258
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=166, random_state=100))])
cv score: [0.62643678 0.65517241 0.62068966 0.68087855 0.39664083]
----------------------------------------
Trial 4259
----------------------------------------
Parameters {'rf__n_estimators': 52, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=52,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 4260
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.71695402 0.69252874 0.63436693 0.39534884]
----------------------------------------
Trial 4261
----------------------------------------
Parameters {'rf__n_estimators': 110, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=110,
random_state=100))])
cv score: [0.61637931 0.59841954 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 4262
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=138, random_state=100,
subsample=0.75))])
cv score: [0.61637931 0.59482759 0.45833333 0.63049096 0.47674419]
----------------------------------------
Trial 4263
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=121,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.70258621 0.63793103 0.59431525 0.4005168 ]
----------------------------------------
Trial 4264
----------------------------------------
Parameters {'rf__n_estimators': 57, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=57,
random_state=100))])
cv score: [0.64367816 0.63362069 0.57471264 0.57622739 0.36627907]
----------------------------------------
Trial 4265
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='sqrt',
n_estimators=127, random_state=100,
subsample=0.7))])
cv score: [0.55603448 0.61925287 0.54022989 0.70155039 0.43023256]
----------------------------------------
Trial 4266
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=176,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64367816 0.68247126 0.62356322 0.60852713 0.4250646 ]
----------------------------------------
Trial 4267
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=105,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65301724 0.60201149 0.61925287 0.54392765 0.33850129]
----------------------------------------
Trial 4268
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=28, random_state=100,
subsample=0.6))])
cv score: [0.61494253 0.59770115 0.59770115 0.55684755 0.41602067]
----------------------------------------
Trial 4269
----------------------------------------
Parameters {'rf__n_estimators': 185, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=185,
random_state=100))])
cv score: [0.61637931 0.65229885 0.61063218 0.7118863 0.41472868]
----------------------------------------
Trial 4270
----------------------------------------
Parameters {'rf__n_estimators': 53, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=53, random_state=100))])
cv score: [0.7112069 0.67097701 0.64942529 0.60852713 0.34108527]
----------------------------------------
Trial 4271
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=188, random_state=100))])
cv score: [0.63793103 0.6637931 0.6091954 0.67054264 0.39664083]
----------------------------------------
Trial 4272
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
max_features='log2',
n_estimators=109, random_state=100,
subsample=0.8))])
cv score: [0.70258621 0.6566092 0.66666667 0.63436693 0.42377261]
----------------------------------------
Trial 4273
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=19, random_state=100))])
cv score: [0.69396552 0.62643678 0.70258621 0.50839793 0.43669251]
----------------------------------------
Trial 4274
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68247126 0.61925287 0.67241379 0.64211886 0.38372093]
----------------------------------------
Trial 4275
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=72, random_state=100))])
cv score: [0.64511494 0.60201149 0.56465517 0.59173127 0.4496124 ]
----------------------------------------
Trial 4276
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56465517 0.72988506 0.56465517 0.58656331 0.44444444]
----------------------------------------
Trial 4277
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70545977 0.59770115 0.68103448 0.59043928 0.4005168 ]
----------------------------------------
Trial 4278
----------------------------------------
Parameters {'gb__n_estimators': 55, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
n_estimators=55, random_state=100,
subsample=0.85))])
cv score: [0.56034483 0.72844828 0.58477011 0.65503876 0.43023256]
----------------------------------------
Trial 4279
----------------------------------------
Parameters {'xgb__n_estimators': 176, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=176,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68390805 0.65804598 0.68103448 0.63953488 0.45736434]
----------------------------------------
Trial 4280
----------------------------------------
Parameters {'xgb__n_estimators': 20, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=20,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63362069 0.59195402 0.70761494 0.64664083 0.3494832 ]
----------------------------------------
Trial 4281
----------------------------------------
Parameters {'rf__n_estimators': 84, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=84, random_state=100))])
cv score: [0.55675287 0.6637931 0.5783046 0.58850129 0.45542636]
----------------------------------------
Trial 4282
----------------------------------------
Parameters {'gb__n_estimators': 90, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=13,
max_features='sqrt',
n_estimators=90, random_state=100,
subsample=0.95))])
cv score: [0.61494253 0.65948276 0.56465517 0.6873385 0.45865633]
----------------------------------------
Trial 4283
----------------------------------------
Parameters {'xgb__n_estimators': 118, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=118,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76077586 0.64295977 0.63505747 0.62919897 0.43669251]
----------------------------------------
Trial 4284
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.85))])
cv score: [0.72844828 0.66810345 0.60632184 0.67958656 0.44186047]
----------------------------------------
Trial 4285
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=106,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64798851 0.68678161 0.72988506 0.63049096 0.41537468]
----------------------------------------
Trial 4286
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=98, random_state=100))])
cv score: [0.56106322 0.69181034 0.5941092 0.6001292 0.45090439]
----------------------------------------
Trial 4287
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
n_estimators=160, random_state=100,
subsample=0.8))])
cv score: [0.62212644 0.74137931 0.62356322 0.70155039 0.39405685]
----------------------------------------
Trial 4288
----------------------------------------
Parameters {'xgb__n_estimators': 156, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=156,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.69109195 0.55603448 0.64211886 0.40697674]
----------------------------------------
Trial 4289
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, n_estimators=167,
random_state=100,
subsample=0.85))])
cv score: [0.56896552 0.73275862 0.61063218 0.61886305 0.43281654]
----------------------------------------
Trial 4290
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6795977 0.67241379 0.6795977 0.60077519 0.40826873]
----------------------------------------
Trial 4291
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70689655 0.67241379 0.63362069 0.62919897 0.41731266]
----------------------------------------
Trial 4292
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.70833333 0.5387931 0.5620155 0.45348837]
----------------------------------------
Trial 4293
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77155172 0.58908046 0.70689655 0.62338501 0.46317829]
----------------------------------------
Trial 4294
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=87,
random_state=100))])
cv score: [0.61422414 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 4295
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.9))])
cv score: [0.53735632 0.66522989 0.54885057 0.64470284 0.44702842]
----------------------------------------
Trial 4296
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=136, random_state=100))])
cv score: [0.65229885 0.67241379 0.61350575 0.59560724 0.43669251]
----------------------------------------
Trial 4297
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
n_estimators=198, random_state=100,
subsample=0.9))])
cv score: [0.60488506 0.72126437 0.4683908 0.71705426 0.48578811]
----------------------------------------
Trial 4298
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=186,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.70545977 0.57183908 0.62273902 0.42894057]
----------------------------------------
Trial 4299
----------------------------------------
Parameters {'xgb__n_estimators': 175, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=175,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56034483 0.6795977 0.52873563 0.55943152 0.45219638]
----------------------------------------
Trial 4300
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.72701149 0.67528736 0.54651163 0.43023256]
----------------------------------------
Trial 4301
----------------------------------------
Parameters {'xgb__n_estimators': 174, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=174,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56609195 0.70689655 0.64798851 0.58268734 0.44702842]
----------------------------------------
Trial 4302
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.6954023 0.67672414 0.65633075 0.47416021]
----------------------------------------
Trial 4303
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=111,
random_state=100))])
cv score: [0.63649425 0.59770115 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 4304
----------------------------------------
Parameters {'xgb__n_estimators': 61, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=61,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7183908 0.58189655 0.72844828 0.62403101 0.39147287]
----------------------------------------
Trial 4305
----------------------------------------
Parameters {'xgb__n_estimators': 166, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=166,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6795977 0.6637931 0.65229885 0.67312661 0.42118863]
----------------------------------------
Trial 4306
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=96,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79166667 0.61781609 0.6875 0.63630491 0.41989664]
----------------------------------------
Trial 4307
----------------------------------------
Parameters {'gb__n_estimators': 154, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
n_estimators=154, random_state=100,
subsample=0.7))])
cv score: [0.69396552 0.625 0.49856322 0.64211886 0.47674419]
----------------------------------------
Trial 4308
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5704023 0.72270115 0.58764368 0.6124031 0.38501292]
----------------------------------------
Trial 4309
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=10,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62284483 0.56106322 0.52442529 0.55490956 0.39470284]
----------------------------------------
Trial 4310
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.73132184 0.63936782 0.64728682 0.41989664]
----------------------------------------
Trial 4311
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=109,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55603448 0.73706897 0.69252874 0.57364341 0.40826873]
----------------------------------------
Trial 4312
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='sqrt',
n_estimators=51, random_state=100,
subsample=0.9))])
cv score: [0.77873563 0.58908046 0.73706897 0.66149871 0.39405685]
----------------------------------------
Trial 4313
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.7112069 0.56896552 0.56589147 0.45348837]
----------------------------------------
Trial 4314
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=2,
max_features='log2',
n_estimators=25, random_state=100,
subsample=0.7))])
cv score: [0.86206897 0.62859195 0.68103448 0.65245478 0.36821705]
----------------------------------------
Trial 4315
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.80316092 0.59626437 0.68678161 0.63049096 0.5 ]
----------------------------------------
Trial 4316
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=118, random_state=100))])
cv score: [0.64798851 0.67097701 0.55172414 0.66408269 0.37080103]
----------------------------------------
Trial 4317
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=194,
random_state=100))])
cv score: [0.61781609 0.65517241 0.60488506 0.67571059 0.45219638]
----------------------------------------
Trial 4318
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='sqrt',
n_estimators=95,
random_state=100))])
cv score: [0.62643678 0.68678161 0.62931034 0.68992248 0.4250646 ]
----------------------------------------
Trial 4319
----------------------------------------
Parameters {'xgb__n_estimators': 115, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=115,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7808908 0.58189655 0.65876437 0.5503876 0.42248062]
----------------------------------------
Trial 4320
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.73994253 0.60057471 0.57751938 0.41731266]
----------------------------------------
Trial 4321
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=134,
random_state=100))])
cv score: [0.69683908 0.64942529 0.65229885 0.64728682 0.43281654]
----------------------------------------
Trial 4322
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=65, random_state=100,
subsample=0.8))])
cv score: [0.51005747 0.76149425 0.59051724 0.6744186 0.45219638]
----------------------------------------
Trial 4323
----------------------------------------
Parameters {'xgb__n_estimators': 33, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=33,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.67816092 0.6566092 0.59431525 0.39534884]
----------------------------------------
Trial 4324
----------------------------------------
Parameters {'xgb__n_estimators': 153, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=153,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79382184 0.60847701 0.66666667 0.62661499 0.43217054]
----------------------------------------
Trial 4325
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69252874 0.59913793 0.6566092 0.58010336 0.43863049]
----------------------------------------
Trial 4326
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=168, random_state=100))])
cv score: [0.72701149 0.63793103 0.65948276 0.64599483 0.41472868]
----------------------------------------
Trial 4327
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=38, random_state=100,
subsample=0.65))])
cv score: [0.6637931 0.60632184 0.53304598 0.68475452 0.46770026]
----------------------------------------
Trial 4328
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=166, random_state=100,
subsample=0.6))])
cv score: [0.64511494 0.6408046 0.50143678 0.69767442 0.43023256]
----------------------------------------
Trial 4329
----------------------------------------
Parameters {'rf__n_estimators': 83, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=83,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 4330
----------------------------------------
Parameters {'xgb__n_estimators': 114, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=114,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.68534483 0.53017241 0.65891473 0.4250646 ]
----------------------------------------
Trial 4331
----------------------------------------
Parameters {'xgb__n_estimators': 23, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=23,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.65373563 0.72701149 0.58074935 0.40762274]
----------------------------------------
Trial 4332
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=159, random_state=100))])
cv score: [0.64224138 0.65229885 0.63362069 0.68604651 0.40310078]
----------------------------------------
Trial 4333
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=4,
n_estimators=193, random_state=100,
subsample=0.85))])
cv score: [0.67241379 0.64511494 0.43534483 0.66149871 0.44832041]
----------------------------------------
Trial 4334
----------------------------------------
Parameters {'rf__n_estimators': 188, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=188, random_state=100))])
cv score: [0.72557471 0.6408046 0.63936782 0.6124031 0.40439276]
----------------------------------------
Trial 4335
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.6, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=156, random_state=100,
subsample=0.6))])
cv score: [0.55316092 0.60775862 0.48275862 0.65116279 0.46124031]
----------------------------------------
Trial 4336
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=114, random_state=100))])
cv score: [0.73132184 0.63793103 0.65517241 0.57364341 0.37855297]
----------------------------------------
Trial 4337
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=13,
n_estimators=29, random_state=100,
subsample=0.8))])
cv score: [0.56896552 0.37068966 0.67528736 0.68863049 0.37209302]
----------------------------------------
Trial 4338
----------------------------------------
Parameters {'rf__n_estimators': 159, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=159,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 4339
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71264368 0.66091954 0.68821839 0.60335917 0.44250646]
----------------------------------------
Trial 4340
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=17,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62068966 0.62787356 0.55172414 0.61111111 0.40568475]
----------------------------------------
Trial 4341
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7808908 0.59913793 0.62859195 0.5497416 0.39082687]
----------------------------------------
Trial 4342
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=121,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.76867816 0.58764368 0.58010336 0.41602067]
----------------------------------------
Trial 4343
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=17, random_state=100,
subsample=0.65))])
cv score: [0.52298851 0.7183908 0.65229885 0.75452196 0.50258398]
----------------------------------------
Trial 4344
----------------------------------------
Parameters {'rf__n_estimators': 63, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=63, random_state=100))])
cv score: [0.5704023 0.70689655 0.6329023 0.6873385 0.4373385 ]
----------------------------------------
Trial 4345
----------------------------------------
Parameters {'gb__n_estimators': 173, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=8,
max_features='sqrt',
n_estimators=173, random_state=100,
subsample=0.7))])
cv score: [0.65086207 0.65086207 0.44827586 0.59560724 0.44056848]
----------------------------------------
Trial 4346
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=96, random_state=100))])
cv score: [0.59770115 0.67600575 0.64942529 0.68475452 0.4379845 ]
----------------------------------------
Trial 4347
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.8, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
max_features='sqrt',
n_estimators=109, random_state=100,
subsample=0.8))])
cv score: [0.52298851 0.55172414 0.51293103 0.62790698 0.48837209]
----------------------------------------
Trial 4348
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=157, random_state=100))])
cv score: [0.56465517 0.64942529 0.57758621 0.65374677 0.43540052]
----------------------------------------
Trial 4349
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.71264368 0.74425287 0.63436693 0.37855297]
----------------------------------------
Trial 4350
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.75, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=95, random_state=100,
subsample=0.75))])
cv score: [0.72270115 0.64511494 0.58045977 0.62661499 0.48449612]
----------------------------------------
Trial 4351
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=194,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77155172 0.65804598 0.68821839 0.68475452 0.4250646 ]
----------------------------------------
Trial 4352
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
max_features='sqrt',
n_estimators=44, random_state=100,
subsample=0.6))])
cv score: [0.58333333 0.64511494 0.6566092 0.62661499 0.48837209]
----------------------------------------
Trial 4353
----------------------------------------
Parameters {'xgb__n_estimators': 105, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=105,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74856322 0.70402299 0.72198276 0.63953488 0.39534884]
----------------------------------------
Trial 4354
----------------------------------------
Parameters {'gb__n_estimators': 101, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=101, random_state=100,
subsample=0.6))])
cv score: [0.5933908 0.6637931 0.57471264 0.68346253 0.4496124 ]
----------------------------------------
Trial 4355
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=5,
max_features='log2',
n_estimators=129, random_state=100,
subsample=0.75))])
cv score: [0.62643678 0.44971264 0.57902299 0.43281654 0.52583979]
----------------------------------------
Trial 4356
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76077586 0.59554598 0.63218391 0.63113695 0.40116279]
----------------------------------------
Trial 4357
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=158, random_state=100))])
cv score: [0.56465517 0.65517241 0.58045977 0.66020672 0.43540052]
----------------------------------------
Trial 4358
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=41, random_state=100))])
cv score: [0.58692529 0.70545977 0.61422414 0.69702842 0.43281654]
----------------------------------------
Trial 4359
----------------------------------------
Parameters {'rf__n_estimators': 78, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=78, random_state=100))])
cv score: [0.77729885 0.62859195 0.53951149 0.56847545 0.39664083]
----------------------------------------
Trial 4360
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.95))])
cv score: [0.54597701 0.66522989 0.45114943 0.63307494 0.4250646 ]
----------------------------------------
Trial 4361
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.75))])
cv score: [0.57327586 0.65804598 0.45545977 0.63565891 0.50904393]
----------------------------------------
Trial 4362
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=12,
n_estimators=121, random_state=100,
subsample=0.8))])
cv score: [0.59051724 0.74568966 0.59051724 0.68992248 0.43540052]
----------------------------------------
Trial 4363
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=193,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.68247126 0.625 0.61627907 0.39793282]
----------------------------------------
Trial 4364
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=21,
random_state=100))])
cv score: [0.56321839 0.60847701 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 4365
----------------------------------------
Parameters {'gb__n_estimators': 108, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='log2',
n_estimators=108, random_state=100,
subsample=0.95))])
cv score: [0.69683908 0.62643678 0.60632184 0.625323 0.44444444]
----------------------------------------
Trial 4366
----------------------------------------
Parameters {'xgb__n_estimators': 78, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=78,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60201149 0.64367816 0.61063218 0.49095607 0.37984496]
----------------------------------------
Trial 4367
----------------------------------------
Parameters {'rf__n_estimators': 60, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=60,
random_state=100))])
cv score: [0.61422414 0.60201149 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 4368
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=101,
random_state=100))])
cv score: [0.74712644 0.63793103 0.70689655 0.64599483 0.41731266]
----------------------------------------
Trial 4369
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70114943 0.71551724 0.7112069 0.62596899 0.4373385 ]
----------------------------------------
Trial 4370
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=58,
random_state=100))])
cv score: [0.60488506 0.65948276 0.60847701 0.69379845 0.4121447 ]
----------------------------------------
Trial 4371
----------------------------------------
Parameters {'gb__n_estimators': 113, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='log2',
n_estimators=113, random_state=100,
subsample=0.65))])
cv score: [0.62356322 0.56321839 0.68247126 0.56847545 0.50516796]
----------------------------------------
Trial 4372
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=17,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78807471 0.59554598 0.61781609 0.54586563 0.38824289]
----------------------------------------
Trial 4373
----------------------------------------
Parameters {'xgb__n_estimators': 55, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=55,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.71408046 0.66954023 0.65374677 0.41989664]
----------------------------------------
Trial 4374
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=42, random_state=100,
subsample=0.8))])
cv score: [0.58764368 0.76149425 0.60344828 0.69509044 0.30232558]
----------------------------------------
Trial 4375
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=193, random_state=100,
subsample=0.7))])
cv score: [0.72701149 0.70833333 0.62068966 0.63565891 0.44573643]
----------------------------------------
Trial 4376
----------------------------------------
Parameters {'gb__n_estimators': 185, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=185, random_state=100,
subsample=0.85))])
cv score: [0.60775862 0.66666667 0.52298851 0.60335917 0.50516796]
----------------------------------------
Trial 4377
----------------------------------------
Parameters {'xgb__n_estimators': 70, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=70,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77729885 0.57327586 0.71695402 0.66989664 0.42635659]
----------------------------------------
Trial 4378
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='log2',
n_estimators=98, random_state=100))])
cv score: [0.60057471 0.65804598 0.57902299 0.62015504 0.44702842]
----------------------------------------
Trial 4379
----------------------------------------
Parameters {'gb__n_estimators': 193, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='sqrt',
n_estimators=193, random_state=100,
subsample=0.9))])
cv score: [0.67385057 0.67241379 0.54310345 0.66020672 0.45865633]
----------------------------------------
Trial 4380
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=11,
n_estimators=44, random_state=100,
subsample=0.7))])
cv score: [0.52155172 0.69109195 0.52011494 0.75839793 0.39664083]
----------------------------------------
Trial 4381
----------------------------------------
Parameters {'rf__n_estimators': 36, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=36,
random_state=100))])
cv score: [0.70402299 0.61925287 0.65948276 0.66925065 0.39534884]
----------------------------------------
Trial 4382
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58477011 0.69971264 0.66954023 0.64082687 0.39922481]
----------------------------------------
Trial 4383
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=197, random_state=100))])
cv score: [0.59482759 0.72485632 0.67385057 0.68217054 0.43992248]
----------------------------------------
Trial 4384
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=44, random_state=100))])
cv score: [0.58045977 0.71336207 0.63721264 0.71382429 0.4244186 ]
----------------------------------------
Trial 4385
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='sqrt',
n_estimators=77, random_state=100,
subsample=0.75))])
cv score: [0.5158046 0.58764368 0.40086207 0.51808786 0.37209302]
----------------------------------------
Trial 4386
----------------------------------------
Parameters {'rf__n_estimators': 112, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=112,
random_state=100))])
cv score: [0.64008621 0.59626437 0.56178161 0.51744186 0.41860465]
----------------------------------------
Trial 4387
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, n_estimators=157,
random_state=100,
subsample=0.95))])
cv score: [0.57183908 0.68965517 0.46982759 0.73514212 0.44832041]
----------------------------------------
Trial 4388
----------------------------------------
Parameters {'xgb__n_estimators': 159, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=159,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60201149 0.67241379 0.59051724 0.59560724 0.45607235]
----------------------------------------
Trial 4389
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=91, random_state=100))])
cv score: [0.72126437 0.68103448 0.61637931 0.64728682 0.46124031]
----------------------------------------
Trial 4390
----------------------------------------
Parameters {'rf__n_estimators': 131, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=131,
random_state=100))])
cv score: [0.61206897 0.59841954 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 4391
----------------------------------------
Parameters {'rf__n_estimators': 181, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=181, random_state=100))])
cv score: [0.61206897 0.66954023 0.58908046 0.62273902 0.44573643]
----------------------------------------
Trial 4392
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.61350575 0.56609195 0.64857881 0.41343669]
----------------------------------------
Trial 4393
----------------------------------------
Parameters {'rf__n_estimators': 139, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=139, random_state=100))])
cv score: [0.57112069 0.69037356 0.60991379 0.60077519 0.44186047]
----------------------------------------
Trial 4394
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74353448 0.57758621 0.64727011 0.55232558 0.3998708 ]
----------------------------------------
Trial 4395
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56321839 0.63936782 0.58908046 0.56847545 0.42635659]
----------------------------------------
Trial 4396
----------------------------------------
Parameters {'gb__n_estimators': 163, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
n_estimators=163, random_state=100,
subsample=0.6))])
cv score: [0.56465517 0.70689655 0.6091954 0.68475452 0.46124031]
----------------------------------------
Trial 4397
----------------------------------------
Parameters {'xgb__n_estimators': 75, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=75,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.63362069 0.5545977 0.51679587 0.44832041]
----------------------------------------
Trial 4398
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=118, random_state=100))])
cv score: [0.56609195 0.69683908 0.61637931 0.59689922 0.44832041]
----------------------------------------
Trial 4399
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=115,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 4400
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='log2',
n_estimators=132, random_state=100,
subsample=0.95))])
cv score: [0.67097701 0.67672414 0.62931034 0.65374677 0.46124031]
----------------------------------------
Trial 4401
----------------------------------------
Parameters {'gb__n_estimators': 166, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
n_estimators=166, random_state=100,
subsample=0.9))])
cv score: [0.70689655 0.66882184 0.60632184 0.63565891 0.42377261]
----------------------------------------
Trial 4402
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=12, random_state=100))])
cv score: [0.5387931 0.66307471 0.61278736 0.62080103 0.41472868]
----------------------------------------
Trial 4403
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=179, random_state=100,
subsample=0.75))])
cv score: [0.57471264 0.66810345 0.45258621 0.60206718 0.42635659]
----------------------------------------
Trial 4404
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=160, random_state=100,
subsample=0.85))])
cv score: [0.60201149 0.6795977 0.51149425 0.66537468 0.4625323 ]
----------------------------------------
Trial 4405
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=171, random_state=100))])
cv score: [0.63936782 0.66235632 0.61063218 0.61757106 0.45607235]
----------------------------------------
Trial 4406
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=51,
random_state=100))])
cv score: [0.67672414 0.62931034 0.67385057 0.6744186 0.3875969 ]
----------------------------------------
Trial 4407
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=117,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.68390805 0.66522989 0.56718346 0.43927649]
----------------------------------------
Trial 4408
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=65,
random_state=100))])
cv score: [0.63721264 0.60201149 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 4409
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=143,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.67385057 0.53735632 0.61111111 0.4005168 ]
----------------------------------------
Trial 4410
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=137,
random_state=100))])
cv score: [0.58979885 0.60272989 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 4411
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=151,
random_state=100))])
cv score: [0.61422414 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 4412
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=150,
random_state=100))])
cv score: [0.76724138 0.63218391 0.67600575 0.65374677 0.40697674]
----------------------------------------
Trial 4413
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=22, random_state=100,
subsample=0.95))])
cv score: [0.45977011 0.6637931 0.48563218 0.61369509 0.46124031]
----------------------------------------
Trial 4414
----------------------------------------
Parameters {'xgb__n_estimators': 77, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=77,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76867816 0.58908046 0.72198276 0.66731266 0.41149871]
----------------------------------------
Trial 4415
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.9))])
cv score: [0.58333333 0.68678161 0.5 0.62144703 0.41860465]
----------------------------------------
Trial 4416
----------------------------------------
Parameters {'gb__n_estimators': 141, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=141, random_state=100,
subsample=0.9))])
cv score: [0.62356322 0.68678161 0.54597701 0.68863049 0.4379845 ]
----------------------------------------
Trial 4417
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features=None,
n_estimators=76, random_state=100))])
cv score: [0.60488506 0.68103448 0.67816092 0.67700258 0.4502584 ]
----------------------------------------
Trial 4418
----------------------------------------
Parameters {'xgb__n_estimators': 32, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=32,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.59770115 0.54741379 0.5503876 0.35658915]
----------------------------------------
Trial 4419
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=97, random_state=100,
subsample=0.85))])
cv score: [0.54741379 0.67241379 0.48706897 0.63178295 0.39664083]
----------------------------------------
Trial 4420
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=12,
max_features='sqrt',
n_estimators=71, random_state=100,
subsample=0.75))])
cv score: [0.45545977 0.61063218 0.56896552 0.5994832 0.37726098]
----------------------------------------
Trial 4421
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=130,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.7112069 0.57758621 0.58010336 0.46640827]
----------------------------------------
Trial 4422
----------------------------------------
Parameters {'rf__n_estimators': 176, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='sqrt',
n_estimators=176, random_state=100))])
cv score: [0.57183908 0.66810345 0.58333333 0.62080103 0.42958656]
----------------------------------------
Trial 4423
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=45, random_state=100,
subsample=0.9))])
cv score: [0.65948276 0.61781609 0.49568966 0.66408269 0.51550388]
----------------------------------------
Trial 4424
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.66666667 0.67672414 0.61627907 0.44832041]
----------------------------------------
Trial 4425
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.5545977 0.36350575 0.5626615 0.39082687]
----------------------------------------
Trial 4426
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62068966 0.68965517 0.45977011 0.61498708 0.47416021]
----------------------------------------
Trial 4427
----------------------------------------
Parameters {'xgb__n_estimators': 129, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=129,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56034483 0.74425287 0.61494253 0.67958656 0.44832041]
----------------------------------------
Trial 4428
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, n_estimators=99,
random_state=100,
subsample=0.65))])
cv score: [0.69396552 0.69827586 0.63218391 0.73126615 0.48191214]
----------------------------------------
Trial 4429
----------------------------------------
Parameters {'gb__n_estimators': 112, 'gb__subsample': 0.7, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
n_estimators=112, random_state=100,
subsample=0.7))])
cv score: [0.65373563 0.61781609 0.60344828 0.63436693 0.4625323 ]
----------------------------------------
Trial 4430
----------------------------------------
Parameters {'rf__n_estimators': 56, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features=None,
n_estimators=56, random_state=100))])
cv score: [0.56393678 0.70402299 0.64367816 0.69379845 0.43669251]
----------------------------------------
Trial 4431
----------------------------------------
Parameters {'gb__n_estimators': 130, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=13,
n_estimators=130, random_state=100,
subsample=0.9))])
cv score: [0.55890805 0.74137931 0.57183908 0.70930233 0.36563307]
----------------------------------------
Trial 4432
----------------------------------------
Parameters {'gb__n_estimators': 142, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
n_estimators=142, random_state=100,
subsample=0.6))])
cv score: [0.58908046 0.70545977 0.54022989 0.7002584 0.46770026]
----------------------------------------
Trial 4433
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75431034 0.59841954 0.69324713 0.55103359 0.37144703]
----------------------------------------
Trial 4434
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=130, random_state=100))])
cv score: [0.74281609 0.64367816 0.6637931 0.5878553 0.40180879]
----------------------------------------
Trial 4435
----------------------------------------
Parameters {'xgb__n_estimators': 149, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=149,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56034483 0.7112069 0.55316092 0.68217054 0.43023256]
----------------------------------------
Trial 4436
----------------------------------------
Parameters {'xgb__n_estimators': 93, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=93,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75574713 0.64942529 0.69109195 0.64728682 0.42248062]
----------------------------------------
Trial 4437
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77227011 0.5941092 0.74425287 0.625323 0.47868217]
----------------------------------------
Trial 4438
----------------------------------------
Parameters {'rf__n_estimators': 101, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=101,
random_state=100))])
cv score: [0.61350575 0.59913793 0.57686782 0.56976744 0.45348837]
----------------------------------------
Trial 4439
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='sqrt',
n_estimators=43, random_state=100,
subsample=0.65))])
cv score: [0.68103448 0.59051724 0.6091954 0.62790698 0.36111111]
----------------------------------------
Trial 4440
----------------------------------------
Parameters {'xgb__n_estimators': 197, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=197,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76867816 0.59626437 0.70402299 0.62144703 0.44121447]
----------------------------------------
Trial 4441
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.65, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='sqrt',
n_estimators=42, random_state=100,
subsample=0.65))])
cv score: [0.60775862 0.65229885 0.45402299 0.59689922 0.42118863]
----------------------------------------
Trial 4442
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=9,
max_features='log2',
n_estimators=187, random_state=100,
subsample=0.85))])
cv score: [0.63793103 0.66666667 0.47413793 0.65245478 0.46511628]
----------------------------------------
Trial 4443
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=178,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.71551724 0.61350575 0.64211886 0.4121447 ]
----------------------------------------
Trial 4444
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60488506 0.6795977 0.625 0.56330749 0.42764858]
----------------------------------------
Trial 4445
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=89,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72413793 0.67241379 0.69827586 0.66472868 0.4121447 ]
----------------------------------------
Trial 4446
----------------------------------------
Parameters {'xgb__n_estimators': 184, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=184,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57758621 0.66954023 0.61206897 0.56589147 0.39405685]
----------------------------------------
Trial 4447
----------------------------------------
Parameters {'gb__n_estimators': 63, 'gb__subsample': 0.7, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=63, random_state=100,
subsample=0.7))])
cv score: [0.52298851 0.68678161 0.55603448 0.71447028 0.45865633]
----------------------------------------
Trial 4448
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.72413793 0.6954023 0.60658915 0.374677 ]
----------------------------------------
Trial 4449
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71264368 0.65804598 0.71408046 0.59689922 0.35529716]
----------------------------------------
Trial 4450
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=130,
random_state=100))])
cv score: [0.77155172 0.63218391 0.66163793 0.62919897 0.38888889]
----------------------------------------
Trial 4451
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67816092 0.59195402 0.61063218 0.60206718 0.4250646 ]
----------------------------------------
Trial 4452
----------------------------------------
Parameters {'gb__n_estimators': 78, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='sqrt',
n_estimators=78, random_state=100,
subsample=0.95))])
cv score: [0.54166667 0.67385057 0.50143678 0.57105943 0.41860465]
----------------------------------------
Trial 4453
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76221264 0.5933908 0.71336207 0.6375969 0.39534884]
----------------------------------------
Trial 4454
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=32,
random_state=100))])
cv score: [0.55172414 0.56034483 0.54454023 0.70284238 0.40633075]
----------------------------------------
Trial 4455
----------------------------------------
Parameters {'gb__n_estimators': 198, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
n_estimators=198, random_state=100,
subsample=0.9))])
cv score: [0.60632184 0.73563218 0.58908046 0.66925065 0.42377261]
----------------------------------------
Trial 4456
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features=None, n_estimators=189,
random_state=100))])
cv score: [0.58692529 0.60201149 0.51436782 0.52260982 0.46899225]
----------------------------------------
Trial 4457
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=163,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.68534483 0.61063218 0.65245478 0.44444444]
----------------------------------------
Trial 4458
----------------------------------------
Parameters {'rf__n_estimators': 76, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features='sqrt',
n_estimators=76, random_state=100))])
cv score: [0.61925287 0.62212644 0.62212644 0.7002584 0.36821705]
----------------------------------------
Trial 4459
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=145, random_state=100))])
cv score: [0.7183908 0.64367816 0.67241379 0.59819121 0.39793282]
----------------------------------------
Trial 4460
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7816092 0.61494253 0.65086207 0.6130491 0.4748062 ]
----------------------------------------
Trial 4461
----------------------------------------
Parameters {'rf__n_estimators': 34, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=34,
random_state=100))])
cv score: [0.54238506 0.55603448 0.56752874 0.70542636 0.3998708 ]
----------------------------------------
Trial 4462
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=46, random_state=100))])
cv score: [0.75 0.62428161 0.45402299 0.6124031 0.36821705]
----------------------------------------
Trial 4463
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73706897 0.58405172 0.63146552 0.62919897 0.45542636]
----------------------------------------
Trial 4464
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=199,
random_state=100))])
cv score: [0.74568966 0.65229885 0.69683908 0.64599483 0.4379845 ]
----------------------------------------
Trial 4465
----------------------------------------
Parameters {'xgb__n_estimators': 178, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=178,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78376437 0.6091954 0.69109195 0.60981912 0.43087855]
----------------------------------------
Trial 4466
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66666667 0.65517241 0.62643678 0.59689922 0.40956072]
----------------------------------------
Trial 4467
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.65804598 0.66235632 0.62403101 0.4379845 ]
----------------------------------------
Trial 4468
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72557471 0.62356322 0.46551724 0.51614987 0.37790698]
----------------------------------------
Trial 4469
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.71695402 0.62356322 0.64341085 0.43540052]
----------------------------------------
Trial 4470
----------------------------------------
Parameters {'rf__n_estimators': 162, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=162, random_state=100))])
cv score: [0.72557471 0.625 0.66091954 0.61627907 0.41343669]
----------------------------------------
Trial 4471
----------------------------------------
Parameters {'rf__n_estimators': 19, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=19, random_state=100))])
cv score: [0.73275862 0.61206897 0.58189655 0.65116279 0.36692506]
----------------------------------------
Trial 4472
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features=None,
n_estimators=160, random_state=100))])
cv score: [0.60201149 0.72054598 0.64798851 0.68863049 0.42700258]
----------------------------------------
Trial 4473
----------------------------------------
Parameters {'xgb__n_estimators': 170, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=170,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.76436782 0.6091954 0.625 0.64857881 0.46899225]
----------------------------------------
Trial 4474
----------------------------------------
Parameters {'rf__n_estimators': 158, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=158,
random_state=100))])
cv score: [0.61494253 0.67025862 0.60704023 0.72093023 0.40826873]
----------------------------------------
Trial 4475
----------------------------------------
Parameters {'xgb__n_estimators': 82, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=82,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7112069 0.625 0.72988506 0.6124031 0.41731266]
----------------------------------------
Trial 4476
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features=None, n_estimators=190,
random_state=100))])
cv score: [0.64008621 0.6012931 0.56321839 0.51744186 0.41860465]
----------------------------------------
Trial 4477
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.70258621 0.58764368 0.62273902 0.41343669]
----------------------------------------
Trial 4478
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.58189655 0.47126437 0.53359173 0.30943152]
----------------------------------------
Trial 4479
----------------------------------------
Parameters {'xgb__n_estimators': 87, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=87,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67097701 0.60344828 0.6408046 0.59819121 0.44315245]
----------------------------------------
Trial 4480
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=73, random_state=100,
subsample=0.6))])
cv score: [0.73850575 0.67097701 0.7112069 0.59819121 0.39922481]
----------------------------------------
Trial 4481
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=44,
random_state=100))])
cv score: [0.61925287 0.62787356 0.59554598 0.65762274 0.36692506]
----------------------------------------
Trial 4482
----------------------------------------
Parameters {'xgb__n_estimators': 158, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=158,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65373563 0.63649425 0.67241379 0.59819121 0.42118863]
----------------------------------------
Trial 4483
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=58,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58045977 0.74281609 0.54022989 0.63307494 0.40826873]
----------------------------------------
Trial 4484
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=77,
random_state=100))])
cv score: [0.70833333 0.63936782 0.66522989 0.65374677 0.38501292]
----------------------------------------
Trial 4485
----------------------------------------
Parameters {'gb__n_estimators': 170, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='sqrt',
n_estimators=170, random_state=100,
subsample=0.75))])
cv score: [0.61206897 0.71695402 0.55316092 0.63178295 0.44573643]
----------------------------------------
Trial 4486
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
max_features='log2',
n_estimators=156, random_state=100,
subsample=0.85))])
cv score: [0.59051724 0.59770115 0.44971264 0.54651163 0.52067183]
----------------------------------------
Trial 4487
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58764368 0.73994253 0.59626437 0.59431525 0.47157623]
----------------------------------------
Trial 4488
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.72557471 0.59195402 0.67312661 0.43669251]
----------------------------------------
Trial 4489
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=7, max_features='sqrt',
n_estimators=25, random_state=100,
subsample=0.8))])
cv score: [0.63936782 0.56321839 0.59482759 0.56976744 0.48191214]
----------------------------------------
Trial 4490
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=157,
random_state=100))])
cv score: [0.57327586 0.5941092 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 4491
----------------------------------------
Parameters {'xgb__n_estimators': 192, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=192,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.74712644 0.5862069 0.53617571 0.42377261]
----------------------------------------
Trial 4492
----------------------------------------
Parameters {'rf__n_estimators': 69, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=69, random_state=100))])
cv score: [0.59913793 0.62571839 0.55675287 0.60723514 0.42894057]
----------------------------------------
Trial 4493
----------------------------------------
Parameters {'rf__n_estimators': 117, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=117, random_state=100))])
cv score: [0.61135057 0.71551724 0.63721264 0.66343669 0.43217054]
----------------------------------------
Trial 4494
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=171, random_state=100))])
cv score: [0.56321839 0.65373563 0.61278736 0.63049096 0.40439276]
----------------------------------------
Trial 4495
----------------------------------------
Parameters {'rf__n_estimators': 106, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=106, random_state=100))])
cv score: [0.74281609 0.64367816 0.65948276 0.57235142 0.38113695]
----------------------------------------
Trial 4496
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=174,
random_state=100))])
cv score: [0.60632184 0.68965517 0.56752874 0.68604651 0.43152455]
----------------------------------------
Trial 4497
----------------------------------------
Parameters {'xgb__n_estimators': 79, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=79,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.76867816 0.5704023 0.47932817 0.49483204]
----------------------------------------
Trial 4498
----------------------------------------
Parameters {'rf__n_estimators': 107, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=107,
random_state=100))])
cv score: [0.65948276 0.64798851 0.62931034 0.64341085 0.44832041]
----------------------------------------
Trial 4499
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=97, random_state=100,
subsample=0.95))])
cv score: [0.54885057 0.65517241 0.58477011 0.65374677 0.42377261]
----------------------------------------
Trial 4500
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003,
max_features='log2',
n_estimators=45, random_state=100,
subsample=0.7))])
cv score: [0.76293103 0.62643678 0.64224138 0.56718346 0.45994832]
----------------------------------------
Trial 4501
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=199,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74281609 0.63936782 0.67241379 0.64728682 0.44767442]
----------------------------------------
Trial 4502
----------------------------------------
Parameters {'xgb__n_estimators': 138, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=138,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63649425 0.71982759 0.59770115 0.61757106 0.41085271]
----------------------------------------
Trial 4503
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=193,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.66666667 0.57902299 0.67183463 0.44573643]
----------------------------------------
Trial 4504
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
n_estimators=197, random_state=100,
subsample=0.7))])
cv score: [0.57183908 0.72701149 0.57327586 0.71576227 0.43023256]
----------------------------------------
Trial 4505
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=118, random_state=100,
subsample=0.7))])
cv score: [0.73994253 0.65804598 0.5704023 0.64470284 0.5245478 ]
----------------------------------------
Trial 4506
----------------------------------------
Parameters {'gb__n_estimators': 194, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=5,
max_features='log2',
n_estimators=194, random_state=100,
subsample=0.85))])
cv score: [0.64942529 0.70258621 0.58189655 0.67571059 0.50387597]
----------------------------------------
Trial 4507
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.95, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
max_features='sqrt',
n_estimators=65, random_state=100,
subsample=0.95))])
cv score: [0.54454023 0.6637931 0.51149425 0.61369509 0.45348837]
----------------------------------------
Trial 4508
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=20,
random_state=100))])
cv score: [0.74856322 0.64942529 0.60057471 0.72222222 0.45090439]
----------------------------------------
Trial 4509
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='sqrt',
n_estimators=54, random_state=100,
subsample=0.85))])
cv score: [0.65086207 0.66522989 0.55172414 0.625323 0.45090439]
----------------------------------------
Trial 4510
----------------------------------------
Parameters {'rf__n_estimators': 89, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=89, random_state=100))])
cv score: [0.73275862 0.6091954 0.55747126 0.56847545 0.43023256]
----------------------------------------
Trial 4511
----------------------------------------
Parameters {'gb__n_estimators': 55, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=5,
max_features='sqrt',
n_estimators=55,
random_state=100))])
cv score: [0.66810345 0.68103448 0.70114943 0.65116279 0.45348837]
----------------------------------------
Trial 4512
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=134,
random_state=100))])
cv score: [0.54885057 0.66091954 0.5862069 0.74289406 0.42054264]
----------------------------------------
Trial 4513
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=102,
random_state=100))])
cv score: [0.59482759 0.67097701 0.60775862 0.71963824 0.44573643]
----------------------------------------
Trial 4514
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.95, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0,
max_features='sqrt',
n_estimators=153, random_state=100,
subsample=0.95))])
cv score: [0.63649425 0.63649425 0.57183908 0.65762274 0.45865633]
----------------------------------------
Trial 4515
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.66522989 0.52298851 0.60723514 0.46640827]
----------------------------------------
Trial 4516
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=48,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.62212644 0.67241379 0.65891473 0.44444444]
----------------------------------------
Trial 4517
----------------------------------------
Parameters {'gb__n_estimators': 36, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=11,
max_features='log2',
n_estimators=36,
random_state=100))])
cv score: [0.64511494 0.68678161 0.61781609 0.71576227 0.39018088]
----------------------------------------
Trial 4518
----------------------------------------
Parameters {'gb__n_estimators': 13, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=6,
n_estimators=13, random_state=100,
subsample=0.9))])
cv score: [0.55747126 0.61925287 0.42241379 0.71963824 0.44121447]
----------------------------------------
Trial 4519
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=149, random_state=100))])
cv score: [0.73850575 0.65229885 0.66235632 0.64470284 0.42894057]
----------------------------------------
Trial 4520
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 0.95, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
n_estimators=105, random_state=100,
subsample=0.95))])
cv score: [0.55172414 0.70977011 0.61494253 0.65245478 0.41731266]
----------------------------------------
Trial 4521
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=14,
max_features='log2',
n_estimators=159, random_state=100,
subsample=0.75))])
cv score: [0.625 0.67816092 0.59913793 0.59431525 0.45736434]
----------------------------------------
Trial 4522
----------------------------------------
Parameters {'xgb__n_estimators': 76, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=76,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64367816 0.67816092 0.64942529 0.58656331 0.38888889]
----------------------------------------
Trial 4523
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
n_estimators=189, random_state=100,
subsample=0.9))])
cv score: [0.62931034 0.69109195 0.5545977 0.69509044 0.43927649]
----------------------------------------
Trial 4524
----------------------------------------
Parameters {'gb__n_estimators': 28, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
n_estimators=28, random_state=100,
subsample=0.7))])
cv score: [0.70258621 0.64798851 0.6408046 0.6369509 0.4250646 ]
----------------------------------------
Trial 4525
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=6, n_estimators=54,
random_state=100,
subsample=0.85))])
cv score: [0.60344828 0.69827586 0.53304598 0.6744186 0.45348837]
----------------------------------------
Trial 4526
----------------------------------------
Parameters {'xgb__n_estimators': 144, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=144,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.68534483 0.68390805 0.63953488 0.44315245]
----------------------------------------
Trial 4527
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=67,
random_state=100))])
cv score: [0.57614943 0.65804598 0.66882184 0.71382429 0.38824289]
----------------------------------------
Trial 4528
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=132,
random_state=100))])
cv score: [0.74425287 0.64798851 0.6954023 0.64341085 0.43540052]
----------------------------------------
Trial 4529
----------------------------------------
Parameters {'rf__n_estimators': 133, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=133, random_state=100))])
cv score: [0.76149425 0.62787356 0.68390805 0.63565891 0.41860465]
----------------------------------------
Trial 4530
----------------------------------------
Parameters {'xgb__n_estimators': 121, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=121,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.67385057 0.6408046 0.64857881 0.4870801 ]
----------------------------------------
Trial 4531
----------------------------------------
Parameters {'gb__n_estimators': 105, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
n_estimators=105,
random_state=100))])
cv score: [0.58189655 0.65948276 0.58764368 0.75839793 0.39664083]
----------------------------------------
Trial 4532
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67816092 0.64655172 0.6566092 0.63436693 0.40180879]
----------------------------------------
Trial 4533
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=8,
max_features='log2',
n_estimators=47, random_state=100,
subsample=0.65))])
cv score: [0.45977011 0.54166667 0.65804598 0.65245478 0.5374677 ]
----------------------------------------
Trial 4534
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=6,
max_features='sqrt',
n_estimators=46,
random_state=100))])
cv score: [0.64655172 0.72988506 0.58908046 0.625323 0.40697674]
----------------------------------------
Trial 4535
----------------------------------------
Parameters {'rf__n_estimators': 72, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=72, random_state=100))])
cv score: [0.76867816 0.62715517 0.54382184 0.58527132 0.38501292]
----------------------------------------
Trial 4536
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=66, random_state=100,
subsample=0.9))])
cv score: [0.61350575 0.67241379 0.58477011 0.52196382 0.49483204]
----------------------------------------
Trial 4537
----------------------------------------
Parameters {'xgb__n_estimators': 100, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=100,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78520115 0.58261494 0.71479885 0.65116279 0.41085271]
----------------------------------------
Trial 4538
----------------------------------------
Parameters {'gb__n_estimators': 109, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='log2',
n_estimators=109, random_state=100,
subsample=0.65))])
cv score: [0.70545977 0.67672414 0.6795977 0.61627907 0.42248062]
----------------------------------------
Trial 4539
----------------------------------------
Parameters {'rf__n_estimators': 177, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=177, random_state=100))])
cv score: [0.57614943 0.66882184 0.58333333 0.62015504 0.42700258]
----------------------------------------
Trial 4540
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
n_estimators=145, random_state=100,
subsample=0.7))])
cv score: [0.625 0.69109195 0.58333333 0.69896641 0.43152455]
----------------------------------------
Trial 4541
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
n_estimators=97,
random_state=100))])
cv score: [0.64295977 0.6012931 0.56968391 0.51744186 0.40116279]
----------------------------------------
Trial 4542
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=71,
random_state=100))])
cv score: [0.68318966 0.57399425 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 4543
----------------------------------------
Parameters {'gb__n_estimators': 187, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
n_estimators=187, random_state=100,
subsample=0.85))])
cv score: [0.59913793 0.68821839 0.56609195 0.77390181 0.44573643]
----------------------------------------
Trial 4544
----------------------------------------
Parameters {'xgb__n_estimators': 199, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=199,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63793103 0.67672414 0.66235632 0.62661499 0.43669251]
----------------------------------------
Trial 4545
----------------------------------------
Parameters {'gb__n_estimators': 120, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=120, random_state=100,
subsample=0.9))])
cv score: [0.62787356 0.68678161 0.51293103 0.60465116 0.40568475]
----------------------------------------
Trial 4546
----------------------------------------
Parameters {'xgb__n_estimators': 59, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=59,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6954023 0.625 0.67385057 0.59431525 0.45478036]
----------------------------------------
Trial 4547
----------------------------------------
Parameters {'gb__n_estimators': 66, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=66, random_state=100,
subsample=0.9))])
cv score: [0.63936782 0.62643678 0.51149425 0.68346253 0.4870801 ]
----------------------------------------
Trial 4548
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69971264 0.6408046 0.63074713 0.60852713 0.39147287]
----------------------------------------
Trial 4549
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.76436782 0.62356322 0.73385013 0.44573643]
----------------------------------------
Trial 4550
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=10,
max_features='sqrt',
n_estimators=191, random_state=100,
subsample=0.85))])
cv score: [0.59913793 0.67672414 0.43965517 0.6124031 0.51162791]
----------------------------------------
Trial 4551
----------------------------------------
Parameters {'xgb__n_estimators': 68, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=68,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.70114943 0.61925287 0.62015504 0.45090439]
----------------------------------------
Trial 4552
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.65, 'gb__learning_rate': 1.0, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=6,
max_features='sqrt',
n_estimators=117, random_state=100,
subsample=0.65))])
cv score: [0.41163793 0.43390805 0.73850575 0.65245478 0.47609819]
----------------------------------------
Trial 4553
----------------------------------------
Parameters {'gb__n_estimators': 159, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
max_features='sqrt',
n_estimators=159, random_state=100,
subsample=0.85))])
cv score: [0.67097701 0.68965517 0.65373563 0.63307494 0.42764858]
----------------------------------------
Trial 4554
----------------------------------------
Parameters {'xgb__n_estimators': 168, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=168,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.68247126 0.54885057 0.625323 0.4121447 ]
----------------------------------------
Trial 4555
----------------------------------------
Parameters {'rf__n_estimators': 15, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=15, random_state=100))])
cv score: [0.53448276 0.6558908 0.56609195 0.59237726 0.3998708 ]
----------------------------------------
Trial 4556
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=16, random_state=100))])
cv score: [0.60272989 0.63362069 0.52155172 0.5620155 0.39147287]
----------------------------------------
Trial 4557
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=12, random_state=100))])
cv score: [0.6875 0.67025862 0.47916667 0.66020672 0.32364341]
----------------------------------------
Trial 4558
----------------------------------------
Parameters {'xgb__n_estimators': 83, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=83,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.6637931 0.66091954 0.57235142 0.45736434]
----------------------------------------
Trial 4559
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73275862 0.60201149 0.6170977 0.57105943 0.41537468]
----------------------------------------
Trial 4560
----------------------------------------
Parameters {'xgb__n_estimators': 47, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=47,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55172414 0.63649425 0.65517241 0.63307494 0.41085271]
----------------------------------------
Trial 4561
----------------------------------------
Parameters {'gb__n_estimators': 69, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=69, random_state=100,
subsample=0.6))])
cv score: [0.70977011 0.63362069 0.56178161 0.66020672 0.43281654]
----------------------------------------
Trial 4562
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.85, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='log2',
n_estimators=125, random_state=100,
subsample=0.85))])
cv score: [0.66810345 0.73275862 0.56321839 0.63824289 0.50258398]
----------------------------------------
Trial 4563
----------------------------------------
Parameters {'gb__n_estimators': 72, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=10,
max_features='log2',
n_estimators=72, random_state=100,
subsample=0.65))])
cv score: [0.57758621 0.61206897 0.69827586 0.58139535 0.4625323 ]
----------------------------------------
Trial 4564
----------------------------------------
Parameters {'xgb__n_estimators': 17, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=17,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.56034483 0.54741379 0.63178295 0.41666667]
----------------------------------------
Trial 4565
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=42,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.6795977 0.54022989 0.64857881 0.37726098]
----------------------------------------
Trial 4566
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=169, random_state=100))])
cv score: [0.72413793 0.62068966 0.6566092 0.61627907 0.41343669]
----------------------------------------
Trial 4567
----------------------------------------
Parameters {'rf__n_estimators': 16, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=16,
random_state=100))])
cv score: [0.61853448 0.59841954 0.57543103 0.57105943 0.45348837]
----------------------------------------
Trial 4568
----------------------------------------
Parameters {'rf__n_estimators': 104, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=104, random_state=100))])
cv score: [0.75431034 0.65517241 0.6408046 0.64857881 0.43540052]
----------------------------------------
Trial 4569
----------------------------------------
Parameters {'gb__n_estimators': 110, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=2,
max_features='sqrt',
n_estimators=110, random_state=100,
subsample=0.7))])
cv score: [0.43247126 0.67025862 0.32399425 0.69896641 0.54263566]
----------------------------------------
Trial 4570
----------------------------------------
Parameters {'xgb__n_estimators': 126, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=126,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78735632 0.57112069 0.72054598 0.55167959 0.39857881]
----------------------------------------
Trial 4571
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.72413793 0.58908046 0.59431525 0.46705426]
----------------------------------------
Trial 4572
----------------------------------------
Parameters {'xgb__n_estimators': 35, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.07, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=35,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70617816 0.59123563 0.4920977 0.55297158 0.39793282]
----------------------------------------
Trial 4573
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=190,
random_state=100))])
cv score: [0.72126437 0.65229885 0.59195402 0.63953488 0.43410853]
----------------------------------------
Trial 4574
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=40,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66810345 0.69109195 0.61206897 0.61111111 0.49095607]
----------------------------------------
Trial 4575
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='sqrt',
n_estimators=43, random_state=100,
subsample=0.8))])
cv score: [0.75 0.59770115 0.62356322 0.62273902 0.44702842]
----------------------------------------
Trial 4576
----------------------------------------
Parameters {'rf__n_estimators': 168, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='sqrt',
n_estimators=168, random_state=100))])
cv score: [0.72701149 0.63793103 0.65948276 0.64599483 0.41472868]
----------------------------------------
Trial 4577
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=143,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.52011494 0.70402299 0.5387931 0.49095607 0.45865633]
----------------------------------------
Trial 4578
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=61, random_state=100))])
cv score: [0.63936782 0.60488506 0.58764368 0.58397933 0.45607235]
----------------------------------------
Trial 4579
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
max_features='log2',
n_estimators=196, random_state=100,
subsample=0.85))])
cv score: [0.61781609 0.66091954 0.51293103 0.67054264 0.46124031]
----------------------------------------
Trial 4580
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.59913793 0.61350575 0.63436693 0.39018088]
----------------------------------------
Trial 4581
----------------------------------------
Parameters {'rf__n_estimators': 12, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=12, random_state=100))])
cv score: [0.54597701 0.6408046 0.56034483 0.58397933 0.55555556]
----------------------------------------
Trial 4582
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=113, random_state=100))])
cv score: [0.71264368 0.67672414 0.62068966 0.63436693 0.44573643]
----------------------------------------
Trial 4583
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=132,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70114943 0.71551724 0.6637931 0.62015504 0.42054264]
----------------------------------------
Trial 4584
----------------------------------------
Parameters {'rf__n_estimators': 173, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=173,
random_state=100))])
cv score: [0.6795977 0.61566092 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 4585
----------------------------------------
Parameters {'rf__n_estimators': 65, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=65, random_state=100))])
cv score: [0.70258621 0.60632184 0.57614943 0.56072351 0.43023256]
----------------------------------------
Trial 4586
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=42, random_state=100,
subsample=0.65))])
cv score: [0.70545977 0.60344828 0.51149425 0.58914729 0.45090439]
----------------------------------------
Trial 4587
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001,
n_estimators=172, random_state=100,
subsample=0.8))])
cv score: [0.74712644 0.66235632 0.61422414 0.64147287 0.39728682]
----------------------------------------
Trial 4588
----------------------------------------
Parameters {'gb__n_estimators': 119, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=119, random_state=100,
subsample=0.9))])
cv score: [0.58189655 0.60201149 0.59770115 0.66149871 0.41731266]
----------------------------------------
Trial 4589
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56752874 0.62931034 0.59482759 0.59173127 0.3875969 ]
----------------------------------------
Trial 4590
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=5,
max_features='sqrt',
n_estimators=115,
random_state=100))])
cv score: [0.65517241 0.63505747 0.4612069 0.62015504 0.43927649]
----------------------------------------
Trial 4591
----------------------------------------
Parameters {'rf__n_estimators': 113, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=113, random_state=100))])
cv score: [0.62068966 0.7183908 0.63433908 0.66925065 0.41860465]
----------------------------------------
Trial 4592
----------------------------------------
Parameters {'rf__n_estimators': 171, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=171, random_state=100))])
cv score: [0.58477011 0.73347701 0.63433908 0.67829457 0.44186047]
----------------------------------------
Trial 4593
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
max_features='sqrt',
n_estimators=46, random_state=100,
subsample=0.8))])
cv score: [0.68103448 0.68390805 0.45689655 0.55943152 0.45090439]
----------------------------------------
Trial 4594
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt',
random_state=100))])
cv score: [0.61494253 0.6954023 0.63074713 0.71576227 0.41472868]
----------------------------------------
Trial 4595
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=9,
n_estimators=169, random_state=100,
subsample=0.7))])
cv score: [0.55747126 0.57614943 0.37068966 0.61757106 0.49612403]
----------------------------------------
Trial 4596
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.75, 'gb__learning_rate': 1.0, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=13,
max_features='sqrt',
n_estimators=35, random_state=100,
subsample=0.75))])
cv score: [0.63218391 0.6954023 0.62931034 0.73901809 0.44832041]
----------------------------------------
Trial 4597
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.6566092 0.56609195 0.55167959 0.45994832]
----------------------------------------
Trial 4598
----------------------------------------
Parameters {'rf__n_estimators': 190, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='log2', n_estimators=190,
random_state=100))])
cv score: [0.62212644 0.65229885 0.60488506 0.67829457 0.45478036]
----------------------------------------
Trial 4599
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=62,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63074713 0.64942529 0.58908046 0.64470284 0.35529716]
----------------------------------------
Trial 4600
----------------------------------------
Parameters {'xgb__n_estimators': 188, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=188,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60344828 0.65229885 0.625 0.63178295 0.4496124 ]
----------------------------------------
Trial 4601
----------------------------------------
Parameters {'gb__n_estimators': 35, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(n_estimators=35, random_state=100,
subsample=0.8))])
cv score: [0.6566092 0.6954023 0.72844828 0.60788114 0.45090439]
----------------------------------------
Trial 4602
----------------------------------------
Parameters {'rf__n_estimators': 137, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=137, random_state=100))])
cv score: [0.63362069 0.66522989 0.57183908 0.6744186 0.36046512]
----------------------------------------
Trial 4603
----------------------------------------
Parameters {'xgb__n_estimators': 128, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=128,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.70545977 0.65086207 0.60206718 0.46124031]
----------------------------------------
Trial 4604
----------------------------------------
Parameters {'rf__n_estimators': 155, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=155,
random_state=100))])
cv score: [0.61350575 0.59770115 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 4605
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='log2',
n_estimators=68, random_state=100,
subsample=0.85))])
cv score: [0.70402299 0.64511494 0.67241379 0.69638243 0.45090439]
----------------------------------------
Trial 4606
----------------------------------------
Parameters {'gb__n_estimators': 80, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
n_estimators=80, random_state=100,
subsample=0.6))])
cv score: [0.6795977 0.66954023 0.57327586 0.70671835 0.45607235]
----------------------------------------
Trial 4607
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=12,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.6))])
cv score: [0.60201149 0.63793103 0.55172414 0.625323 0.4005168 ]
----------------------------------------
Trial 4608
----------------------------------------
Parameters {'gb__n_estimators': 67, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
n_estimators=67, random_state=100,
subsample=0.85))])
cv score: [0.50862069 0.73132184 0.57183908 0.68346253 0.43152455]
----------------------------------------
Trial 4609
----------------------------------------
Parameters {'gb__n_estimators': 148, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=148, random_state=100,
subsample=0.95))])
cv score: [0.55316092 0.77586207 0.55028736 0.66020672 0.46382429]
----------------------------------------
Trial 4610
----------------------------------------
Parameters {'xgb__n_estimators': 14, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=14,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65804598 0.50431034 0.5862069 0.64082687 0.50516796]
----------------------------------------
Trial 4611
----------------------------------------
Parameters {'xgb__n_estimators': 112, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=112,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.78017241 0.60560345 0.69396552 0.62209302 0.44638243]
----------------------------------------
Trial 4612
----------------------------------------
Parameters {'rf__n_estimators': 115, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=115, random_state=100))])
cv score: [0.75574713 0.63649425 0.66522989 0.58010336 0.38888889]
----------------------------------------
Trial 4613
----------------------------------------
Parameters {'xgb__n_estimators': 11, 'xgb__subsample': 0.9, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=11,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67672414 0.63649425 0.53017241 0.59819121 0.38372093]
----------------------------------------
Trial 4614
----------------------------------------
Parameters {'rf__n_estimators': 180, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=180,
random_state=100))])
cv score: [0.6170977 0.59985632 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 4615
----------------------------------------
Parameters {'xgb__n_estimators': 125, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=125,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.73778736 0.64942529 0.71982759 0.63565891 0.43346253]
----------------------------------------
Trial 4616
----------------------------------------
Parameters {'rf__n_estimators': 199, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=199,
random_state=100))])
cv score: [0.60201149 0.68965517 0.59626437 0.6744186 0.42894057]
----------------------------------------
Trial 4617
----------------------------------------
Parameters {'rf__n_estimators': 194, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=194, random_state=100))])
cv score: [0.74856322 0.63218391 0.69252874 0.6627907 0.42377261]
----------------------------------------
Trial 4618
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=53,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.68390805 0.70977011 0.61111111 0.43927649]
----------------------------------------
Trial 4619
----------------------------------------
Parameters {'xgb__n_estimators': 151, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=151,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62068966 0.72844828 0.55747126 0.68217054 0.39018088]
----------------------------------------
Trial 4620
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=80,
random_state=100))])
cv score: [0.78735632 0.625 0.66307471 0.62919897 0.41989664]
----------------------------------------
Trial 4621
----------------------------------------
Parameters {'rf__n_estimators': 134, 'rf__max_depth': 11, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='log2', n_estimators=134,
random_state=100))])
cv score: [0.58189655 0.68606322 0.58189655 0.66666667 0.42118863]
----------------------------------------
Trial 4622
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=197,
random_state=100))])
cv score: [0.59051724 0.67241379 0.62212644 0.7002584 0.39728682]
----------------------------------------
Trial 4623
----------------------------------------
Parameters {'gb__n_estimators': 33, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='sqrt',
n_estimators=33, random_state=100,
subsample=0.95))])
cv score: [0.59626437 0.54885057 0.47413793 0.68863049 0.50387597]
----------------------------------------
Trial 4624
----------------------------------------
Parameters {'gb__n_estimators': 50, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=8,
n_estimators=50, random_state=100,
subsample=0.6))])
cv score: [0.65517241 0.69971264 0.65804598 0.68346253 0.4754522 ]
----------------------------------------
Trial 4625
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='sqrt',
n_estimators=169, random_state=100,
subsample=0.8))])
cv score: [0.64655172 0.68103448 0.60057471 0.56847545 0.52067183]
----------------------------------------
Trial 4626
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=33,
random_state=100))])
cv score: [0.71551724 0.62068966 0.58764368 0.61757106 0.374677 ]
----------------------------------------
Trial 4627
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=41,
random_state=100))])
cv score: [0.65948276 0.60488506 0.58333333 0.56330749 0.37855297]
----------------------------------------
Trial 4628
----------------------------------------
Parameters {'xgb__n_estimators': 198, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=198,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79094828 0.5933908 0.6954023 0.64082687 0.48126615]
----------------------------------------
Trial 4629
----------------------------------------
Parameters {'rf__n_estimators': 182, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features='log2',
n_estimators=182, random_state=100))])
cv score: [0.7341954 0.64511494 0.64655172 0.64599483 0.41343669]
----------------------------------------
Trial 4630
----------------------------------------
Parameters {'rf__n_estimators': 66, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='log2',
n_estimators=66, random_state=100))])
cv score: [0.62931034 0.60488506 0.58045977 0.59043928 0.43540052]
----------------------------------------
Trial 4631
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=54,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6637931 0.67097701 0.70977011 0.63436693 0.39147287]
----------------------------------------
Trial 4632
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.7, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
n_estimators=121, random_state=100,
subsample=0.7))])
cv score: [0.51867816 0.72557471 0.55028736 0.69896641 0.45478036]
----------------------------------------
Trial 4633
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56896552 0.6795977 0.61063218 0.60723514 0.39147287]
----------------------------------------
Trial 4634
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=167, random_state=100))])
cv score: [0.60488506 0.71623563 0.66810345 0.69896641 0.43540052]
----------------------------------------
Trial 4635
----------------------------------------
Parameters {'rf__n_estimators': 47, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=47, random_state=100))])
cv score: [0.56393678 0.71767241 0.64295977 0.68410853 0.44767442]
----------------------------------------
Trial 4636
----------------------------------------
Parameters {'gb__n_estimators': 195, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='sqrt',
n_estimators=195, random_state=100,
subsample=0.9))])
cv score: [0.65086207 0.67816092 0.5933908 0.54651163 0.51033592]
----------------------------------------
Trial 4637
----------------------------------------
Parameters {'gb__n_estimators': 172, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
n_estimators=172, random_state=100,
subsample=0.85))])
cv score: [0.56178161 0.55890805 0.54741379 0.59689922 0.44832041]
----------------------------------------
Trial 4638
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74281609 0.625 0.66235632 0.63630491 0.40439276]
----------------------------------------
Trial 4639
----------------------------------------
Parameters {'gb__n_estimators': 136, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
n_estimators=136, random_state=100,
subsample=0.75))])
cv score: [0.6091954 0.39942529 0.54885057 0.6873385 0.5129199 ]
----------------------------------------
Trial 4640
----------------------------------------
Parameters {'xgb__n_estimators': 30, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=30,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.65229885 0.63649425 0.57622739 0.45865633]
----------------------------------------
Trial 4641
----------------------------------------
Parameters {'gb__n_estimators': 95, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=95, random_state=100,
subsample=0.85))])
cv score: [0.76293103 0.66522989 0.65732759 0.69379845 0.39534884]
----------------------------------------
Trial 4642
----------------------------------------
Parameters {'xgb__n_estimators': 84, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=84,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67672414 0.59051724 0.57758621 0.55426357 0.47416021]
----------------------------------------
Trial 4643
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56609195 0.71264368 0.61063218 0.58914729 0.46640827]
----------------------------------------
Trial 4644
----------------------------------------
Parameters {'rf__n_estimators': 42, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=42, random_state=100))])
cv score: [0.53951149 0.61494253 0.66235632 0.6124031 0.40568475]
----------------------------------------
Trial 4645
----------------------------------------
Parameters {'xgb__n_estimators': 108, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=108,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.59482759 0.47126437 0.52002584 0.37984496]
----------------------------------------
Trial 4646
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=166,
random_state=100))])
cv score: [0.54310345 0.6558908 0.5862069 0.74160207 0.40374677]
----------------------------------------
Trial 4647
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
random_state=100))])
cv score: [0.62787356 0.66666667 0.61637931 0.65633075 0.41989664]
----------------------------------------
Trial 4648
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=2,
n_estimators=84, random_state=100,
subsample=0.85))])
cv score: [0.71192529 0.67887931 0.56609195 0.63436693 0.40697674]
----------------------------------------
Trial 4649
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.72126437 0.62356322 0.70258621 0.61498708 0.44573643]
----------------------------------------
Trial 4650
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='sqrt', n_estimators=157,
random_state=100))])
cv score: [0.65373563 0.65948276 0.58477011 0.64211886 0.43540052]
----------------------------------------
Trial 4651
----------------------------------------
Parameters {'rf__n_estimators': 58, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=58,
random_state=100))])
cv score: [0.64655172 0.63362069 0.58045977 0.5749354 0.36111111]
----------------------------------------
Trial 4652
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=136,
random_state=100))])
cv score: [0.54454023 0.65948276 0.58333333 0.7377261 0.42054264]
----------------------------------------
Trial 4653
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03,
n_estimators=179,
random_state=100))])
cv score: [0.67385057 0.71695402 0.70689655 0.6124031 0.41537468]
----------------------------------------
Trial 4654
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=197,
random_state=100))])
cv score: [0.59051724 0.67241379 0.62212644 0.7002584 0.39728682]
----------------------------------------
Trial 4655
----------------------------------------
Parameters {'gb__n_estimators': 98, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
max_features='log2',
n_estimators=98, random_state=100,
subsample=0.8))])
cv score: [0.61206897 0.65373563 0.60488506 0.58914729 0.49741602]
----------------------------------------
Trial 4656
----------------------------------------
Parameters {'xgb__n_estimators': 44, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=44,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.68103448 0.60775862 0.65301724 0.56847545 0.42829457]
----------------------------------------
Trial 4657
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=197,
random_state=100))])
cv score: [0.72126437 0.65229885 0.5933908 0.64211886 0.43152455]
----------------------------------------
Trial 4658
----------------------------------------
Parameters {'rf__n_estimators': 64, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='log2',
n_estimators=64, random_state=100))])
cv score: [0.55818966 0.61566092 0.59985632 0.5503876 0.42312661]
----------------------------------------
Trial 4659
----------------------------------------
Parameters {'xgb__n_estimators': 106, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.003, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=106,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6566092 0.65517241 0.64655172 0.57881137 0.39147287]
----------------------------------------
Trial 4660
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
n_estimators=71, random_state=100,
subsample=0.9))])
cv score: [0.5545977 0.76149425 0.58333333 0.62790698 0.33397933]
----------------------------------------
Trial 4661
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
max_features='sqrt',
n_estimators=53, random_state=100,
subsample=0.7))])
cv score: [0.58045977 0.40373563 0.54022989 0.64470284 0.38372093]
----------------------------------------
Trial 4662
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75574713 0.64511494 0.69109195 0.6369509 0.45090439]
----------------------------------------
Trial 4663
----------------------------------------
Parameters {'rf__n_estimators': 38, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=38,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 4664
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
n_estimators=145, random_state=100,
subsample=0.75))])
cv score: [0.51436782 0.73275862 0.58477011 0.67958656 0.44832041]
----------------------------------------
Trial 4665
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=137,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.68247126 0.50143678 0.58268734 0.43540052]
----------------------------------------
Trial 4666
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=25,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71982759 0.56465517 0.63362069 0.71963824 0.39793282]
----------------------------------------
Trial 4667
----------------------------------------
Parameters {'xgb__n_estimators': 190, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=190,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.70833333 0.54741379 0.62661499 0.4250646 ]
----------------------------------------
Trial 4668
----------------------------------------
Parameters {'gb__n_estimators': 25, 'gb__subsample': 0.9, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
n_estimators=25, random_state=100,
subsample=0.9))])
cv score: [0.59626437 0.54597701 0.43390805 0.68346253 0.51162791]
----------------------------------------
Trial 4669
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=163,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57255747 0.67816092 0.74568966 0.6124031 0.38436693]
----------------------------------------
Trial 4670
----------------------------------------
Parameters {'rf__n_estimators': 96, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=96,
random_state=100))])
cv score: [0.55316092 0.67313218 0.63577586 0.72739018 0.35206718]
----------------------------------------
Trial 4671
----------------------------------------
Parameters {'xgb__n_estimators': 31, 'xgb__subsample': 0.6, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=31,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54454023 0.73132184 0.55028736 0.62144703 0.40439276]
----------------------------------------
Trial 4672
----------------------------------------
Parameters {'gb__n_estimators': 151, 'gb__subsample': 0.7, 'gb__learning_rate': 1.0, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, n_estimators=151,
random_state=100, subsample=0.7))])
cv score: [0.59482759 0.43534483 0.56178161 0.59819121 0.6124031 ]
----------------------------------------
Trial 4673
----------------------------------------
Parameters {'xgb__n_estimators': 90, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.03, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=90,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.67672414 0.66954023 0.66810345 0.58527132 0.44444444]
----------------------------------------
Trial 4674
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=89,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.69971264 0.67385057 0.56459948 0.39018088]
----------------------------------------
Trial 4675
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7,
max_features='sqrt',
n_estimators=180, random_state=100,
subsample=0.8))])
cv score: [0.61350575 0.48706897 0.52011494 0.62273902 0.51356589]
----------------------------------------
Trial 4676
----------------------------------------
Parameters {'gb__n_estimators': 168, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=4,
n_estimators=168,
random_state=100))])
cv score: [0.56034483 0.66091954 0.61350575 0.73643411 0.4379845 ]
----------------------------------------
Trial 4677
----------------------------------------
Parameters {'xgb__n_estimators': 183, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=183,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.71695402 0.61637931 0.65116279 0.38501292]
----------------------------------------
Trial 4678
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=32, random_state=100))])
cv score: [0.59195402 0.70833333 0.60775862 0.58010336 0.50129199]
----------------------------------------
Trial 4679
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=196,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 4680
----------------------------------------
Parameters {'rf__n_estimators': 135, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=135,
random_state=100))])
cv score: [0.59626437 0.67600575 0.63936782 0.71705426 0.39534884]
----------------------------------------
Trial 4681
----------------------------------------
Parameters {'rf__n_estimators': 87, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=87, random_state=100))])
cv score: [0.78304598 0.63002874 0.54094828 0.57816537 0.38501292]
----------------------------------------
Trial 4682
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=97,
random_state=100))])
cv score: [0.63649425 0.59913793 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 4683
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='log2',
n_estimators=156, random_state=100,
subsample=0.85))])
cv score: [0.67097701 0.72270115 0.46408046 0.63307494 0.49483204]
----------------------------------------
Trial 4684
----------------------------------------
Parameters {'xgb__n_estimators': 64, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=64,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70258621 0.5783046 0.63146552 0.55620155 0.37726098]
----------------------------------------
Trial 4685
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
max_features='sqrt',
n_estimators=102, random_state=100,
subsample=0.8))])
cv score: [0.51293103 0.59626437 0.5862069 0.56847545 0.47157623]
----------------------------------------
Trial 4686
----------------------------------------
Parameters {'gb__n_estimators': 65, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=2,
n_estimators=65, random_state=100,
subsample=0.95))])
cv score: [0.73706897 0.68103448 0.6487069 0.68281654 0.37855297]
----------------------------------------
Trial 4687
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=20, random_state=100))])
cv score: [0.51795977 0.63362069 0.55531609 0.65697674 0.44250646]
----------------------------------------
Trial 4688
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=42,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55100575 0.59698276 0.47126437 0.5251938 0.44186047]
----------------------------------------
Trial 4689
----------------------------------------
Parameters {'gb__n_estimators': 46, 'gb__subsample': 0.8, 'gb__learning_rate': 0.03, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=4,
max_features='log2',
n_estimators=46, random_state=100,
subsample=0.8))])
cv score: [0.69109195 0.60632184 0.69252874 0.61886305 0.4754522 ]
----------------------------------------
Trial 4690
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=88,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71408046 0.65229885 0.70043103 0.60852713 0.43927649]
----------------------------------------
Trial 4691
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71695402 0.59482759 0.61494253 0.68281654 0.44702842]
----------------------------------------
Trial 4692
----------------------------------------
Parameters {'xgb__n_estimators': 109, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=109,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61781609 0.6091954 0.61063218 0.62790698 0.37726098]
----------------------------------------
Trial 4693
----------------------------------------
Parameters {'rf__n_estimators': 77, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=77,
random_state=100))])
cv score: [0.61566092 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 4694
----------------------------------------
Parameters {'xgb__n_estimators': 28, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=28,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70545977 0.62787356 0.51724138 0.6369509 0.4754522 ]
----------------------------------------
Trial 4695
----------------------------------------
Parameters {'xgb__n_estimators': 51, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=51,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6566092 0.66522989 0.66235632 0.59043928 0.39276486]
----------------------------------------
Trial 4696
----------------------------------------
Parameters {'xgb__n_estimators': 34, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=34,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.67385057 0.54310345 0.54392765 0.4496124 ]
----------------------------------------
Trial 4697
----------------------------------------
Parameters {'rf__n_estimators': 44, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='sqrt',
n_estimators=44, random_state=100))])
cv score: [0.54454023 0.62643678 0.66810345 0.60852713 0.40568475]
----------------------------------------
Trial 4698
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='log2',
n_estimators=156, random_state=100,
subsample=0.95))])
cv score: [0.63362069 0.65804598 0.51724138 0.63565891 0.5620155 ]
----------------------------------------
Trial 4699
----------------------------------------
Parameters {'xgb__n_estimators': 96, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.1, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=96,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79885057 0.55028736 0.7262931 0.64082687 0.41343669]
----------------------------------------
Trial 4700
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=75, random_state=100,
subsample=0.9))])
cv score: [0.59770115 0.68821839 0.54597701 0.64470284 0.36692506]
----------------------------------------
Trial 4701
----------------------------------------
Parameters {'rf__n_estimators': 167, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=167, random_state=100))])
cv score: [0.56681034 0.67025862 0.58405172 0.61111111 0.43346253]
----------------------------------------
Trial 4702
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=40,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70114943 0.66666667 0.625 0.61111111 0.50322997]
----------------------------------------
Trial 4703
----------------------------------------
Parameters {'gb__n_estimators': 155, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
max_features='sqrt',
n_estimators=155, random_state=100,
subsample=0.9))])
cv score: [0.57758621 0.64367816 0.5158046 0.61498708 0.41085271]
----------------------------------------
Trial 4704
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.85, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=58, random_state=100,
subsample=0.85))])
cv score: [0.61350575 0.64224138 0.56465517 0.63049096 0.4005168 ]
----------------------------------------
Trial 4705
----------------------------------------
Parameters {'xgb__n_estimators': 89, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=89,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61494253 0.70258621 0.58764368 0.6124031 0.39793282]
----------------------------------------
Trial 4706
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=197,
random_state=100))])
cv score: [0.61781609 0.6566092 0.61206897 0.71963824 0.40310078]
----------------------------------------
Trial 4707
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=14,
random_state=100))])
cv score: [0.68031609 0.57399425 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 4708
----------------------------------------
Parameters {'gb__n_estimators': 45, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
n_estimators=45, random_state=100,
subsample=0.75))])
cv score: [0.56321839 0.42385057 0.49712644 0.64211886 0.47803618]
----------------------------------------
Trial 4709
----------------------------------------
Parameters {'gb__n_estimators': 197, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=6,
max_features='sqrt',
n_estimators=197, random_state=100,
subsample=0.75))])
cv score: [0.61350575 0.65373563 0.49856322 0.66408269 0.47416021]
----------------------------------------
Trial 4710
----------------------------------------
Parameters {'gb__n_estimators': 138, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
n_estimators=138, random_state=100,
subsample=0.8))])
cv score: [0.79166667 0.67672414 0.6637931 0.71834625 0.39147287]
----------------------------------------
Trial 4711
----------------------------------------
Parameters {'rf__n_estimators': 128, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features=None,
n_estimators=128, random_state=100))])
cv score: [0.61135057 0.72772989 0.6487069 0.68152455 0.41472868]
----------------------------------------
Trial 4712
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=82,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 4713
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=196,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 4714
----------------------------------------
Parameters {'rf__n_estimators': 20, 'rf__max_depth': 4, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='sqrt', n_estimators=20,
random_state=100))])
cv score: [0.74856322 0.64942529 0.60057471 0.72222222 0.45090439]
----------------------------------------
Trial 4715
----------------------------------------
Parameters {'gb__n_estimators': 87, 'gb__subsample': 0.75, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='sqrt',
n_estimators=87, random_state=100,
subsample=0.75))])
cv score: [0.56752874 0.58045977 0.51436782 0.70671835 0.40568475]
----------------------------------------
Trial 4716
----------------------------------------
Parameters {'gb__n_estimators': 14, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
n_estimators=14, random_state=100,
subsample=0.85))])
cv score: [0.50287356 0.76293103 0.69827586 0.61369509 0.30103359]
----------------------------------------
Trial 4717
----------------------------------------
Parameters {'xgb__n_estimators': 25, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=25,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71048851 0.66810345 0.67097701 0.65374677 0.41085271]
----------------------------------------
Trial 4718
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=134, random_state=100,
subsample=0.6))])
cv score: [0.61494253 0.62212644 0.61781609 0.66020672 0.44444444]
----------------------------------------
Trial 4719
----------------------------------------
Parameters {'xgb__n_estimators': 91, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=91,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62356322 0.65229885 0.61637931 0.59560724 0.41085271]
----------------------------------------
Trial 4720
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='log2', n_estimators=103,
random_state=100))])
cv score: [0.70977011 0.64798851 0.67241379 0.65374677 0.40826873]
----------------------------------------
Trial 4721
----------------------------------------
Parameters {'xgb__n_estimators': 53, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=53,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7112069 0.63793103 0.69971264 0.58656331 0.4618863 ]
----------------------------------------
Trial 4722
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71623563 0.57471264 0.65732759 0.63436693 0.38953488]
----------------------------------------
Trial 4723
----------------------------------------
Parameters {'rf__n_estimators': 88, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features=None,
n_estimators=88, random_state=100))])
cv score: [0.78448276 0.63936782 0.65086207 0.60206718 0.4005168 ]
----------------------------------------
Trial 4724
----------------------------------------
Parameters {'xgb__n_estimators': 181, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=181,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7341954 0.65517241 0.68893678 0.63307494 0.41989664]
----------------------------------------
Trial 4725
----------------------------------------
Parameters {'xgb__n_estimators': 194, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=194,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75862069 0.65086207 0.6795977 0.61046512 0.44315245]
----------------------------------------
Trial 4726
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
max_features='sqrt',
n_estimators=128, random_state=100,
subsample=0.65))])
cv score: [0.63793103 0.65373563 0.5933908 0.68604651 0.45865633]
----------------------------------------
Trial 4727
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.64942529 0.58045977 0.66537468 0.40697674]
----------------------------------------
Trial 4728
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=144, random_state=100,
subsample=0.95))])
cv score: [0.61206897 0.65804598 0.46982759 0.60077519 0.42118863]
----------------------------------------
Trial 4729
----------------------------------------
Parameters {'rf__n_estimators': 198, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=198,
random_state=100))])
cv score: [0.6637931 0.64655172 0.63505747 0.65374677 0.4496124 ]
----------------------------------------
Trial 4730
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69109195 0.68821839 0.6566092 0.6498708 0.47286822]
----------------------------------------
Trial 4731
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.95, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
n_estimators=60, random_state=100,
subsample=0.95))])
cv score: [0.55028736 0.75502874 0.56465517 0.6744186 0.38242894]
----------------------------------------
Trial 4732
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=43, random_state=100))])
cv score: [0.61494253 0.625 0.70689655 0.57235142 0.42118863]
----------------------------------------
Trial 4733
----------------------------------------
Parameters {'xgb__n_estimators': 161, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=161,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59051724 0.7183908 0.61206897 0.60465116 0.41343669]
----------------------------------------
Trial 4734
----------------------------------------
Parameters {'xgb__n_estimators': 143, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.01, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=143,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.5933908 0.6637931 0.66954023 0.61111111 0.3875969 ]
----------------------------------------
Trial 4735
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.6, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
max_features='sqrt',
n_estimators=196, random_state=100,
subsample=0.6))])
cv score: [0.66810345 0.68103448 0.55172414 0.62790698 0.4625323 ]
----------------------------------------
Trial 4736
----------------------------------------
Parameters {'xgb__n_estimators': 196, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=196,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75 0.56537356 0.68390805 0.54328165 0.374677 ]
----------------------------------------
Trial 4737
----------------------------------------
Parameters {'rf__n_estimators': 145, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=145, random_state=100))])
cv score: [0.54597701 0.67097701 0.61422414 0.62144703 0.43217054]
----------------------------------------
Trial 4738
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 8, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features='log2', n_estimators=165,
random_state=100))])
cv score: [0.64942529 0.66522989 0.58045977 0.64082687 0.43281654]
----------------------------------------
Trial 4739
----------------------------------------
Parameters {'xgb__n_estimators': 132, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=132,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.65086207 0.72844828 0.58908046 0.63436693 0.40439276]
----------------------------------------
Trial 4740
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=14,
n_estimators=189, random_state=100,
subsample=0.7))])
cv score: [0.56465517 0.71408046 0.60344828 0.69509044 0.39147287]
----------------------------------------
Trial 4741
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=150,
random_state=100))])
cv score: [0.59913793 0.67816092 0.60272989 0.72868217 0.40568475]
----------------------------------------
Trial 4742
----------------------------------------
Parameters {'gb__n_estimators': 17, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=7,
n_estimators=17, random_state=100,
subsample=0.95))])
cv score: [0.50287356 0.70545977 0.41235632 0.75064599 0.50645995]
----------------------------------------
Trial 4743
----------------------------------------
Parameters {'gb__n_estimators': 88, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=88, random_state=100,
subsample=0.8))])
cv score: [0.66091954 0.60057471 0.55747126 0.63178295 0.50258398]
----------------------------------------
Trial 4744
----------------------------------------
Parameters {'gb__n_estimators': 134, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
max_features='log2',
n_estimators=134,
random_state=100))])
cv score: [0.60775862 0.6795977 0.57327586 0.67829457 0.47803618]
----------------------------------------
Trial 4745
----------------------------------------
Parameters {'xgb__n_estimators': 81, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=81,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70689655 0.68821839 0.69252874 0.60981912 0.42764858]
----------------------------------------
Trial 4746
----------------------------------------
Parameters {'rf__n_estimators': 82, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=82,
random_state=100))])
cv score: [0.78448276 0.62787356 0.66020115 0.62661499 0.41085271]
----------------------------------------
Trial 4747
----------------------------------------
Parameters {'rf__n_estimators': 164, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=164, random_state=100))])
cv score: [0.56537356 0.67241379 0.58405172 0.60981912 0.43863049]
----------------------------------------
Trial 4748
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=130, random_state=100))])
cv score: [0.66810345 0.6795977 0.61063218 0.60206718 0.42894057]
----------------------------------------
Trial 4749
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=97, random_state=100))])
cv score: [0.63505747 0.66522989 0.51724138 0.67054264 0.37338501]
----------------------------------------
Trial 4750
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.65, 'gb__learning_rate': 0.01, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=8,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.65))])
cv score: [0.63936782 0.63362069 0.6091954 0.70284238 0.45865633]
----------------------------------------
Trial 4751
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.03, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.53951149 0.58045977 0.5 0.52002584 0.38049096]
----------------------------------------
Trial 4752
----------------------------------------
Parameters {'rf__n_estimators': 150, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=7, max_features=None,
n_estimators=150, random_state=100))])
cv score: [0.63218391 0.68534483 0.625 0.67700258 0.41602067]
----------------------------------------
Trial 4753
----------------------------------------
Parameters {'rf__n_estimators': 186, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt', n_estimators=186,
random_state=100))])
cv score: [0.59195402 0.67241379 0.62643678 0.69509044 0.39341085]
----------------------------------------
Trial 4754
----------------------------------------
Parameters {'xgb__n_estimators': 54, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.003, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=54,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71623563 0.61206897 0.59051724 0.5626615 0.39082687]
----------------------------------------
Trial 4755
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=86, random_state=100))])
cv score: [0.66235632 0.63505747 0.56465517 0.5994832 0.43927649]
----------------------------------------
Trial 4756
----------------------------------------
Parameters {'rf__n_estimators': 43, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=43,
random_state=100))])
cv score: [0.77586207 0.63793103 0.62140805 0.6498708 0.40439276]
----------------------------------------
Trial 4757
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=7,
n_estimators=83, random_state=100,
subsample=0.9))])
cv score: [0.56034483 0.68678161 0.58045977 0.75193798 0.42377261]
----------------------------------------
Trial 4758
----------------------------------------
Parameters {'rf__n_estimators': 50, 'rf__max_depth': 3, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features=None, n_estimators=50,
random_state=100))])
cv score: [0.71623563 0.64224138 0.51724138 0.5381137 0.42183463]
----------------------------------------
Trial 4759
----------------------------------------
Parameters {'xgb__n_estimators': 177, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=177,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66235632 0.63218391 0.5933908 0.62403101 0.43152455]
----------------------------------------
Trial 4760
----------------------------------------
Parameters {'gb__n_estimators': 153, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=7,
max_features='log2',
n_estimators=153, random_state=100,
subsample=0.75))])
cv score: [0.61637931 0.65229885 0.47557471 0.58914729 0.45736434]
----------------------------------------
Trial 4761
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=71, random_state=100))])
cv score: [0.55675287 0.62571839 0.59123563 0.55555556 0.42054264]
----------------------------------------
Trial 4762
----------------------------------------
Parameters {'gb__n_estimators': 144, 'gb__subsample': 0.75, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='sqrt',
n_estimators=144, random_state=100,
subsample=0.75))])
cv score: [0.58045977 0.63218391 0.4454023 0.63565891 0.48449612]
----------------------------------------
Trial 4763
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(n_estimators=189,
random_state=100))])
cv score: [0.66091954 0.70114943 0.68390805 0.61886305 0.42248062]
----------------------------------------
Trial 4764
----------------------------------------
Parameters {'gb__n_estimators': 43, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
n_estimators=43, random_state=100,
subsample=0.6))])
cv score: [0.61494253 0.62931034 0.61206897 0.61498708 0.39793282]
----------------------------------------
Trial 4765
----------------------------------------
Parameters {'xgb__n_estimators': 123, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=123,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7112069 0.63362069 0.71336207 0.56912145 0.38242894]
----------------------------------------
Trial 4766
----------------------------------------
Parameters {'gb__n_estimators': 171, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=2, max_features='log2',
n_estimators=171, random_state=100,
subsample=0.9))])
cv score: [0.67097701 0.64942529 0.65229885 0.60723514 0.4005168 ]
----------------------------------------
Trial 4767
----------------------------------------
Parameters {'gb__n_estimators': 127, 'gb__subsample': 0.75, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=127, random_state=100,
subsample=0.75))])
cv score: [0.5158046 0.72844828 0.60344828 0.69509044 0.41731266]
----------------------------------------
Trial 4768
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.003, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=42,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54022989 0.69252874 0.73132184 0.59173127 0.34302326]
----------------------------------------
Trial 4769
----------------------------------------
Parameters {'rf__n_estimators': 91, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features=None,
n_estimators=91, random_state=100))])
cv score: [0.67816092 0.67097701 0.60775862 0.63824289 0.4121447 ]
----------------------------------------
Trial 4770
----------------------------------------
Parameters {'rf__n_estimators': 157, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=157, random_state=100))])
cv score: [0.73275862 0.625 0.65373563 0.61369509 0.41731266]
----------------------------------------
Trial 4771
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=191,
random_state=100))])
cv score: [0.77729885 0.62787356 0.69037356 0.64082687 0.40956072]
----------------------------------------
Trial 4772
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=143, random_state=100,
subsample=0.85))])
cv score: [0.62931034 0.73706897 0.63505747 0.64470284 0.51033592]
----------------------------------------
Trial 4773
----------------------------------------
Parameters {'gb__n_estimators': 169, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=169, random_state=100,
subsample=0.65))])
cv score: [0.54022989 0.65948276 0.56465517 0.61757106 0.45736434]
----------------------------------------
Trial 4774
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=77, random_state=100,
subsample=0.8))])
cv score: [0.56465517 0.66522989 0.49856322 0.67183463 0.44832041]
----------------------------------------
Trial 4775
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 3, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_features='sqrt',
n_estimators=62, random_state=100,
subsample=0.9))])
cv score: [0.72701149 0.66954023 0.67816092 0.60852713 0.43410853]
----------------------------------------
Trial 4776
----------------------------------------
Parameters {'gb__n_estimators': 167, 'gb__subsample': 0.9, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
n_estimators=167, random_state=100,
subsample=0.9))])
cv score: [0.68103448 0.65517241 0.52873563 0.6124031 0.51033592]
----------------------------------------
Trial 4777
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 8, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=8, max_features='sqrt',
n_estimators=165, random_state=100))])
cv score: [0.63793103 0.66091954 0.60775862 0.61627907 0.45736434]
----------------------------------------
Trial 4778
----------------------------------------
Parameters {'gb__n_estimators': 132, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=4,
max_features='sqrt',
n_estimators=132,
random_state=100))])
cv score: [0.6566092 0.63793103 0.55172414 0.61757106 0.54263566]
----------------------------------------
Trial 4779
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59195402 0.70114943 0.62787356 0.60981912 0.4121447 ]
----------------------------------------
Trial 4780
----------------------------------------
Parameters {'gb__n_estimators': 44, 'gb__subsample': 0.7, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=44, random_state=100,
subsample=0.7))])
cv score: [0.68678161 0.65804598 0.47270115 0.58914729 0.44315245]
----------------------------------------
Trial 4781
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=12, max_features='sqrt',
n_estimators=197, random_state=100))])
cv score: [0.57758621 0.66810345 0.61566092 0.65503876 0.42764858]
----------------------------------------
Trial 4782
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=111, random_state=100,
subsample=0.85))])
cv score: [0.59482759 0.66091954 0.52442529 0.64857881 0.41602067]
----------------------------------------
Trial 4783
----------------------------------------
Parameters {'rf__n_estimators': 85, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=85,
random_state=100))])
cv score: [0.70833333 0.65229885 0.6566092 0.65762274 0.37855297]
----------------------------------------
Trial 4784
----------------------------------------
Parameters {'rf__n_estimators': 28, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=28,
random_state=100))])
cv score: [0.66091954 0.63936782 0.59051724 0.5878553 0.45090439]
----------------------------------------
Trial 4785
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=10,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.76436782 0.55028736 0.58656331 0.3501292 ]
----------------------------------------
Trial 4786
----------------------------------------
Parameters {'gb__n_estimators': 42, 'gb__subsample': 0.95, 'gb__learning_rate': 0.3, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=2,
max_features='sqrt',
n_estimators=42, random_state=100,
subsample=0.95))])
cv score: [0.70258621 0.6566092 0.70258621 0.62661499 0.42764858]
----------------------------------------
Trial 4787
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features=None, n_estimators=33,
random_state=100))])
cv score: [0.68318966 0.57686782 0.47413793 0.67700258 0.4121447 ]
----------------------------------------
Trial 4788
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features=None, n_estimators=136,
random_state=100))])
cv score: [0.63577586 0.59770115 0.56609195 0.51744186 0.40116279]
----------------------------------------
Trial 4789
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.65, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.001, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62787356 0.72413793 0.54022989 0.52713178 0.36434109]
----------------------------------------
Trial 4790
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=9, n_estimators=48,
random_state=100, subsample=0.7))])
cv score: [0.5545977 0.7341954 0.52586207 0.72351421 0.44315245]
----------------------------------------
Trial 4791
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=14,
max_features='sqrt',
n_estimators=158, random_state=100,
subsample=0.65))])
cv score: [0.59482759 0.65948276 0.58908046 0.62919897 0.49095607]
----------------------------------------
Trial 4792
----------------------------------------
Parameters {'rf__n_estimators': 31, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=31,
random_state=100))])
cv score: [0.57255747 0.59985632 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 4793
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=113,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69109195 0.65373563 0.69252874 0.62661499 0.43540052]
----------------------------------------
Trial 4794
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=13,
n_estimators=12, random_state=100,
subsample=0.7))])
cv score: [0.51293103 0.56896552 0.48275862 0.60981912 0.40503876]
----------------------------------------
Trial 4795
----------------------------------------
Parameters {'rf__n_estimators': 73, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=73,
random_state=100))])
cv score: [0.6954023 0.63218391 0.66235632 0.65633075 0.37338501]
----------------------------------------
Trial 4796
----------------------------------------
Parameters {'rf__n_estimators': 80, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='log2', n_estimators=80,
random_state=100))])
cv score: [0.58836207 0.66954023 0.65301724 0.69896641 0.37273902]
----------------------------------------
Trial 4797
----------------------------------------
Parameters {'gb__n_estimators': 47, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=11,
n_estimators=47, random_state=100,
subsample=0.9))])
cv score: [0.5933908 0.74568966 0.56034483 0.68217054 0.42377261]
----------------------------------------
Trial 4798
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.7, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='sqrt',
n_estimators=48, random_state=100,
subsample=0.7))])
cv score: [0.59770115 0.72844828 0.54310345 0.65116279 0.48837209]
----------------------------------------
Trial 4799
----------------------------------------
Parameters {'xgb__n_estimators': 117, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=117,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61637931 0.71695402 0.61637931 0.63178295 0.39405685]
----------------------------------------
Trial 4800
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 7, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='log2', n_estimators=97,
random_state=100))])
cv score: [0.64798851 0.63793103 0.62787356 0.63307494 0.42764858]
----------------------------------------
Trial 4801
----------------------------------------
Parameters {'gb__n_estimators': 76, 'gb__subsample': 0.9, 'gb__learning_rate': 1.0, 'gb__max_depth': 10, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=10,
max_features='sqrt',
n_estimators=76, random_state=100,
subsample=0.9))])
cv score: [0.46982759 0.67385057 0.39798851 0.58914729 0.38630491]
----------------------------------------
Trial 4802
----------------------------------------
Parameters {'gb__n_estimators': 97, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=2,
n_estimators=97, random_state=100,
subsample=0.9))])
cv score: [0.68821839 0.67385057 0.71551724 0.62015504 0.37726098]
----------------------------------------
Trial 4803
----------------------------------------
Parameters {'xgb__n_estimators': 40, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=40,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.65517241 0.5545977 0.57622739 0.41343669]
----------------------------------------
Trial 4804
----------------------------------------
Parameters {'rf__n_estimators': 174, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=174, random_state=100))])
cv score: [0.67241379 0.66954023 0.64224138 0.68217054 0.45478036]
----------------------------------------
Trial 4805
----------------------------------------
Parameters {'gb__n_estimators': 68, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, n_estimators=68,
random_state=100, subsample=0.9))])
cv score: [0.66666667 0.68965517 0.75143678 0.65310078 0.40180879]
----------------------------------------
Trial 4806
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.69827586 0.60632184 0.56589147 0.37596899]
----------------------------------------
Trial 4807
----------------------------------------
Parameters {'gb__n_estimators': 84, 'gb__subsample': 0.95, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='log2',
n_estimators=84, random_state=100,
subsample=0.95))])
cv score: [0.58045977 0.71264368 0.54166667 0.64082687 0.40310078]
----------------------------------------
Trial 4808
----------------------------------------
Parameters {'rf__n_estimators': 46, 'rf__max_depth': 13, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features=None,
n_estimators=46, random_state=100))])
cv score: [0.56393678 0.7183908 0.64008621 0.67958656 0.44315245]
----------------------------------------
Trial 4809
----------------------------------------
Parameters {'gb__n_estimators': 12, 'gb__subsample': 0.9, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
n_estimators=12, random_state=100,
subsample=0.9))])
cv score: [0.52442529 0.82112069 0.43821839 0.70542636 0.39599483]
----------------------------------------
Trial 4810
----------------------------------------
Parameters {'gb__n_estimators': 116, 'gb__subsample': 0.95, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
max_features='sqrt',
n_estimators=116, random_state=100,
subsample=0.95))])
cv score: [0.64655172 0.71264368 0.64367816 0.58010336 0.52971576]
----------------------------------------
Trial 4811
----------------------------------------
Parameters {'xgb__n_estimators': 60, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=60,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61206897 0.7341954 0.61925287 0.64728682 0.4379845 ]
----------------------------------------
Trial 4812
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.07, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.6795977 0.72557471 0.63372093 0.3998708 ]
----------------------------------------
Trial 4813
----------------------------------------
Parameters {'rf__n_estimators': 74, 'rf__max_depth': 9, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features=None,
n_estimators=74, random_state=100))])
cv score: [0.58045977 0.69683908 0.64367816 0.67183463 0.43863049]
----------------------------------------
Trial 4814
----------------------------------------
Parameters {'gb__n_estimators': 60, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=8,
max_features='log2',
n_estimators=60, random_state=100,
subsample=0.7))])
cv score: [0.63936782 0.65086207 0.42528736 0.65503876 0.45348837]
----------------------------------------
Trial 4815
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=29, random_state=100))])
cv score: [0.55962644 0.62356322 0.51077586 0.58204134 0.47932817]
----------------------------------------
Trial 4816
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, max_features='sqrt',
n_estimators=27, random_state=100,
subsample=0.65))])
cv score: [0.60775862 0.60057471 0.64798851 0.59043928 0.54521964]
----------------------------------------
Trial 4817
----------------------------------------
Parameters {'xgb__n_estimators': 113, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=113,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.70258621 0.6795977 0.58527132 0.38630491]
----------------------------------------
Trial 4818
----------------------------------------
Parameters {'xgb__n_estimators': 103, 'xgb__subsample': 0.85, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=103,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60057471 0.72413793 0.59482759 0.48578811 0.44186047]
----------------------------------------
Trial 4819
----------------------------------------
Parameters {'rf__n_estimators': 165, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=165,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 4820
----------------------------------------
Parameters {'gb__n_estimators': 196, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=5,
max_features='sqrt',
n_estimators=196, random_state=100,
subsample=0.65))])
cv score: [0.68534483 0.66666667 0.62068966 0.61757106 0.41989664]
----------------------------------------
Trial 4821
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.3, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=71,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66522989 0.64511494 0.70258621 0.66020672 0.39534884]
----------------------------------------
Trial 4822
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07,
max_features='log2',
n_estimators=102, random_state=100,
subsample=0.7))])
cv score: [0.75143678 0.64224138 0.63793103 0.61111111 0.48191214]
----------------------------------------
Trial 4823
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='sqrt',
n_estimators=118, random_state=100))])
cv score: [0.64798851 0.67097701 0.55172414 0.66408269 0.37080103]
----------------------------------------
Trial 4824
----------------------------------------
Parameters {'rf__n_estimators': 93, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=93, random_state=100))])
cv score: [0.73706897 0.62787356 0.66666667 0.57751938 0.39534884]
----------------------------------------
Trial 4825
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
n_estimators=51, random_state=100,
subsample=0.8))])
cv score: [0.53304598 0.44683908 0.60775862 0.60981912 0.42894057]
----------------------------------------
Trial 4826
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=161,
random_state=100))])
cv score: [0.61637931 0.59841954 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 4827
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='log2', n_estimators=11,
random_state=100))])
cv score: [0.76652299 0.65732759 0.71264368 0.66472868 0.49677003]
----------------------------------------
Trial 4828
----------------------------------------
Parameters {'rf__n_estimators': 99, 'rf__max_depth': 12, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features=None, n_estimators=99,
random_state=100))])
cv score: [0.61637931 0.59841954 0.5625 0.51744186 0.46963824]
----------------------------------------
Trial 4829
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 5, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=5, max_features='sqrt',
n_estimators=181,
random_state=100))])
cv score: [0.63505747 0.71408046 0.5 0.55167959 0.42764858]
----------------------------------------
Trial 4830
----------------------------------------
Parameters {'xgb__n_estimators': 65, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.03, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=65,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64224138 0.65804598 0.61063218 0.60335917 0.38242894]
----------------------------------------
Trial 4831
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 8, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=8, max_features='log2',
n_estimators=121, random_state=100,
subsample=0.65))])
cv score: [0.62643678 0.60632184 0.46982759 0.60594315 0.45348837]
----------------------------------------
Trial 4832
----------------------------------------
Parameters {'gb__n_estimators': 102, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=102, random_state=100,
subsample=0.6))])
cv score: [0.64224138 0.60488506 0.5933908 0.64599483 0.46770026]
----------------------------------------
Trial 4833
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 6, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=6,
n_estimators=117, random_state=100,
subsample=0.75))])
cv score: [0.5933908 0.68103448 0.5387931 0.69509044 0.45090439]
----------------------------------------
Trial 4834
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=10,
max_features='log2',
n_estimators=118, random_state=100,
subsample=0.65))])
cv score: [0.61063218 0.60344828 0.57183908 0.67312661 0.45736434]
----------------------------------------
Trial 4835
----------------------------------------
Parameters {'xgb__n_estimators': 154, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.001, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=154,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.71551724 0.55172414 0.58139535 0.44315245]
----------------------------------------
Trial 4836
----------------------------------------
Parameters {'xgb__n_estimators': 135, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.1, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=135,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.81034483 0.59841954 0.67097701 0.61821705 0.46640827]
----------------------------------------
Trial 4837
----------------------------------------
Parameters {'rf__n_estimators': 103, 'rf__max_depth': 9, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=9, max_features='log2',
n_estimators=103, random_state=100))])
cv score: [0.6091954 0.67528736 0.60201149 0.61886305 0.42635659]
----------------------------------------
Trial 4838
----------------------------------------
Parameters {'xgb__n_estimators': 58, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=58,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57902299 0.66522989 0.60344828 0.62015504 0.41731266]
----------------------------------------
Trial 4839
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='log2',
n_estimators=10, random_state=100))])
cv score: [0.78591954 0.66235632 0.56896552 0.5122739 0.42700258]
----------------------------------------
Trial 4840
----------------------------------------
Parameters {'gb__n_estimators': 29, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=29, random_state=100,
subsample=0.85))])
cv score: [0.72844828 0.63649425 0.67672414 0.71834625 0.46640827]
----------------------------------------
Trial 4841
----------------------------------------
Parameters {'rf__n_estimators': 130, 'rf__max_depth': 14, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features=None,
n_estimators=130, random_state=100))])
cv score: [0.59913793 0.72485632 0.64367816 0.66795866 0.42377261]
----------------------------------------
Trial 4842
----------------------------------------
Parameters {'xgb__n_estimators': 136, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=136,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74425287 0.64798851 0.66522989 0.64082687 0.45090439]
----------------------------------------
Trial 4843
----------------------------------------
Parameters {'xgb__n_estimators': 98, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.003, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=98,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74425287 0.67528736 0.70617816 0.64018088 0.40245478]
----------------------------------------
Trial 4844
----------------------------------------
Parameters {'gb__n_estimators': 121, 'gb__subsample': 0.75, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=121, random_state=100,
subsample=0.75))])
cv score: [0.59770115 0.68534483 0.50431034 0.64599483 0.41860465]
----------------------------------------
Trial 4845
----------------------------------------
Parameters {'xgb__n_estimators': 46, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=46,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.79094828 0.53735632 0.6795977 0.64728682 0.39211886]
----------------------------------------
Trial 4846
----------------------------------------
Parameters {'gb__n_estimators': 158, 'gb__subsample': 1.0, 'gb__learning_rate': 0.003, 'gb__max_depth': 6, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=6,
max_features='sqrt',
n_estimators=158,
random_state=100))])
cv score: [0.70114943 0.68534483 0.64367816 0.62919897 0.4379845 ]
----------------------------------------
Trial 4847
----------------------------------------
Parameters {'rf__n_estimators': 61, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features=None, n_estimators=61,
random_state=100))])
cv score: [0.69037356 0.56106322 0.52298851 0.54263566 0.4127907 ]
----------------------------------------
Trial 4848
----------------------------------------
Parameters {'xgb__n_estimators': 88, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.01, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=88,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59770115 0.63649425 0.66522989 0.65245478 0.38630491]
----------------------------------------
Trial 4849
----------------------------------------
Parameters {'gb__n_estimators': 190, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
n_estimators=190, random_state=100,
subsample=0.8))])
cv score: [0.58045977 0.74425287 0.58477011 0.71576227 0.42894057]
----------------------------------------
Trial 4850
----------------------------------------
Parameters {'gb__n_estimators': 62, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
max_features='log2',
n_estimators=62, random_state=100,
subsample=0.6))])
cv score: [0.67816092 0.61350575 0.4454023 0.64341085 0.42764858]
----------------------------------------
Trial 4851
----------------------------------------
Parameters {'xgb__n_estimators': 10, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=10,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62931034 0.70043103 0.58189655 0.58268734 0.49354005]
----------------------------------------
Trial 4852
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.6, 'gb__learning_rate': 0.003, 'gb__max_depth': 4, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=4,
max_features='log2',
n_estimators=92, random_state=100,
subsample=0.6))])
cv score: [0.75143678 0.62787356 0.63649425 0.61757106 0.45348837]
----------------------------------------
Trial 4853
----------------------------------------
Parameters {'xgb__n_estimators': 97, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=97,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54885057 0.65804598 0.5933908 0.65633075 0.38888889]
----------------------------------------
Trial 4854
----------------------------------------
Parameters {'gb__n_estimators': 129, 'gb__subsample': 0.85, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='log2',
n_estimators=129, random_state=100,
subsample=0.85))])
cv score: [0.60632184 0.66810345 0.56896552 0.62144703 0.43152455]
----------------------------------------
Trial 4855
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='sqrt', n_estimators=136,
random_state=100))])
cv score: [0.73132184 0.64655172 0.58764368 0.63565891 0.41860465]
----------------------------------------
Trial 4856
----------------------------------------
Parameters {'xgb__n_estimators': 41, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.03, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=41,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.69971264 0.62787356 0.63362069 0.62790698 0.4496124 ]
----------------------------------------
Trial 4857
----------------------------------------
Parameters {'rf__n_estimators': 41, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=41,
random_state=100))])
cv score: [0.61637931 0.64655172 0.6329023 0.6744186 0.39147287]
----------------------------------------
Trial 4858
----------------------------------------
Parameters {'rf__n_estimators': 153, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=2,
max_features='sqrt', n_estimators=153,
random_state=100))])
cv score: [0.77155172 0.63218391 0.68175287 0.65633075 0.4121447 ]
----------------------------------------
Trial 4859
----------------------------------------
Parameters {'xgb__n_estimators': 165, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=165,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.57471264 0.6954023 0.58764368 0.60077519 0.42764858]
----------------------------------------
Trial 4860
----------------------------------------
Parameters {'rf__n_estimators': 102, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=102, random_state=100))])
cv score: [0.74281609 0.61494253 0.60057471 0.5994832 0.42635659]
----------------------------------------
Trial 4861
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='sqrt',
n_estimators=30, random_state=100))])
cv score: [0.59698276 0.71767241 0.58189655 0.59173127 0.51356589]
----------------------------------------
Trial 4862
----------------------------------------
Parameters {'gb__n_estimators': 48, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=7,
n_estimators=48, random_state=100,
subsample=0.8))])
cv score: [0.28017241 0.62787356 0.49281609 0.6627907 0.39793282]
----------------------------------------
Trial 4863
----------------------------------------
Parameters {'xgb__n_estimators': 49, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=49,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74497126 0.6487069 0.71264368 0.58850129 0.4373385 ]
----------------------------------------
Trial 4864
----------------------------------------
Parameters {'rf__n_estimators': 111, 'rf__max_depth': 2, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='log2',
n_estimators=111, random_state=100))])
cv score: [0.72844828 0.64798851 0.65804598 0.57881137 0.37984496]
----------------------------------------
Trial 4865
----------------------------------------
Parameters {'rf__n_estimators': 10, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=10, random_state=100))])
cv score: [0.46264368 0.6875 0.58548851 0.58979328 0.48837209]
----------------------------------------
Trial 4866
----------------------------------------
Parameters {'rf__n_estimators': 132, 'rf__max_depth': 11, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features=None, n_estimators=132,
random_state=100))])
cv score: [0.61278736 0.59841954 0.58405172 0.56976744 0.45348837]
----------------------------------------
Trial 4867
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=12,
max_features='sqrt',
n_estimators=191, random_state=100,
subsample=0.8))])
cv score: [0.61494253 0.64511494 0.52873563 0.6744186 0.49095607]
----------------------------------------
Trial 4868
----------------------------------------
Parameters {'rf__n_estimators': 51, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='log2',
n_estimators=51, random_state=100))])
cv score: [0.66810345 0.61637931 0.48706897 0.61757106 0.41602067]
----------------------------------------
Trial 4869
----------------------------------------
Parameters {'gb__n_estimators': 189, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=14,
n_estimators=189, random_state=100,
subsample=0.8))])
cv score: [0.55890805 0.53448276 0.51867816 0.67054264 0.38501292]
----------------------------------------
Trial 4870
----------------------------------------
Parameters {'xgb__n_estimators': 163, 'xgb__subsample': 0.7, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.3, 'xgb__max_depth': 7, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=7,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=163,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61925287 0.64224138 0.5933908 0.60852713 0.43669251]
----------------------------------------
Trial 4871
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.3, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77011494 0.57758621 0.68821839 0.60142119 0.44767442]
----------------------------------------
Trial 4872
----------------------------------------
Parameters {'xgb__n_estimators': 50, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=50,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.62212644 0.64798851 0.56847545 0.4496124 ]
----------------------------------------
Trial 4873
----------------------------------------
Parameters {'gb__n_estimators': 71, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
n_estimators=71,
random_state=100))])
cv score: [0.64152299 0.60272989 0.56034483 0.51744186 0.41860465]
----------------------------------------
Trial 4874
----------------------------------------
Parameters {'xgb__n_estimators': 16, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.001, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=16,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.70545977 0.59626437 0.57399425 0.5626615 0.40503876]
----------------------------------------
Trial 4875
----------------------------------------
Parameters {'rf__n_estimators': 169, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=169, random_state=100))])
cv score: [0.72557471 0.65517241 0.64798851 0.66925065 0.43669251]
----------------------------------------
Trial 4876
----------------------------------------
Parameters {'xgb__n_estimators': 157, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.1, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=157,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6408046 0.59051724 0.60057471 0.50645995 0.40310078]
----------------------------------------
Trial 4877
----------------------------------------
Parameters {'gb__n_estimators': 181, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 5, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=5,
n_estimators=181, random_state=100,
subsample=0.85))])
cv score: [0.60488506 0.66522989 0.48850575 0.62403101 0.44444444]
----------------------------------------
Trial 4878
----------------------------------------
Parameters {'gb__n_estimators': 53, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 5, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=5,
max_features='log2',
n_estimators=53, random_state=100,
subsample=0.8))])
cv score: [0.68821839 0.64224138 0.49281609 0.63565891 0.41343669]
----------------------------------------
Trial 4879
----------------------------------------
Parameters {'gb__n_estimators': 22, 'gb__subsample': 0.6, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=22, random_state=100,
subsample=0.6))])
cv score: [0.6954023 0.65517241 0.52586207 0.56589147 0.47674419]
----------------------------------------
Trial 4880
----------------------------------------
Parameters {'xgb__n_estimators': 127, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.3, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=127,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.7658046 0.6012931 0.71479885 0.55103359 0.40245478]
----------------------------------------
Trial 4881
----------------------------------------
Parameters {'xgb__n_estimators': 180, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=180,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64942529 0.73850575 0.5316092 0.5994832 0.42635659]
----------------------------------------
Trial 4882
----------------------------------------
Parameters {'gb__n_estimators': 32, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=9,
n_estimators=32, random_state=100,
subsample=0.8))])
cv score: [0.55316092 0.65373563 0.54885057 0.65245478 0.3630491 ]
----------------------------------------
Trial 4883
----------------------------------------
Parameters {'gb__n_estimators': 75, 'gb__subsample': 0.85, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=75, random_state=100,
subsample=0.85))])
cv score: [0.70833333 0.68821839 0.6875 0.65762274 0.42958656]
----------------------------------------
Trial 4884
----------------------------------------
Parameters {'xgb__n_estimators': 130, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=130,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.54310345 0.63362069 0.60057471 0.57881137 0.45090439]
----------------------------------------
Trial 4885
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.75, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
max_features='log2',
n_estimators=94, random_state=100,
subsample=0.75))])
cv score: [0.5933908 0.64224138 0.56465517 0.62403101 0.45736434]
----------------------------------------
Trial 4886
----------------------------------------
Parameters {'gb__n_estimators': 128, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=10,
max_features='log2',
n_estimators=128, random_state=100,
subsample=0.95))])
cv score: [0.58189655 0.70689655 0.5545977 0.67700258 0.42377261]
----------------------------------------
Trial 4887
----------------------------------------
Parameters {'xgb__n_estimators': 186, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.07, 'xgb__gamma': 0.07, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.07,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=186,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58189655 0.71408046 0.67816092 0.63178295 0.35788114]
----------------------------------------
Trial 4888
----------------------------------------
Parameters {'gb__n_estimators': 99, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
max_features='log2',
n_estimators=99, random_state=100,
subsample=0.8))])
cv score: [0.63505747 0.65804598 0.53735632 0.6873385 0.39534884]
----------------------------------------
Trial 4889
----------------------------------------
Parameters {'gb__n_estimators': 51, 'gb__subsample': 0.8, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
n_estimators=51, random_state=100,
subsample=0.8))])
cv score: [0.62643678 0.39942529 0.46982759 0.63178295 0.26614987]
----------------------------------------
Trial 4890
----------------------------------------
Parameters {'gb__n_estimators': 161, 'gb__subsample': 0.6, 'gb__learning_rate': 0.03, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=14,
max_features='sqrt',
n_estimators=161, random_state=100,
subsample=0.6))])
cv score: [0.63649425 0.63649425 0.58045977 0.66020672 0.47286822]
----------------------------------------
Trial 4891
----------------------------------------
Parameters {'gb__n_estimators': 30, 'gb__subsample': 0.9, 'gb__learning_rate': 0.01, 'gb__max_depth': 2, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=2,
max_features='log2',
n_estimators=30, random_state=100,
subsample=0.9))])
cv score: [0.77227011 0.58979885 0.7341954 0.66795866 0.37144703]
----------------------------------------
Trial 4892
----------------------------------------
Parameters {'gb__n_estimators': 27, 'gb__subsample': 0.6, 'gb__learning_rate': 0.3, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=14,
max_features='sqrt',
n_estimators=27, random_state=100,
subsample=0.6))])
cv score: [0.5387931 0.63218391 0.44396552 0.57235142 0.52583979]
----------------------------------------
Trial 4893
----------------------------------------
Parameters {'gb__n_estimators': 100, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=4, max_features='sqrt',
random_state=100,
subsample=0.65))])
cv score: [0.68534483 0.6566092 0.59051724 0.68217054 0.53875969]
----------------------------------------
Trial 4894
----------------------------------------
Parameters {'xgb__n_estimators': 39, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.03, 'xgb__gamma': 0.3, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.03,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=39,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6566092 0.69109195 0.69683908 0.65116279 0.37855297]
----------------------------------------
Trial 4895
----------------------------------------
Parameters {'gb__n_estimators': 125, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='sqrt',
n_estimators=125, random_state=100,
subsample=0.65))])
cv score: [0.60632184 0.67528736 0.5933908 0.59431525 0.48320413]
----------------------------------------
Trial 4896
----------------------------------------
Parameters {'xgb__n_estimators': 107, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=107,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.66091954 0.72126437 0.59482759 0.59043928 0.41472868]
----------------------------------------
Trial 4897
----------------------------------------
Parameters {'rf__n_estimators': 13, 'rf__max_depth': 6, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features=None, n_estimators=13,
random_state=100))])
cv score: [0.66810345 0.62212644 0.5158046 0.70865633 0.45930233]
----------------------------------------
Trial 4898
----------------------------------------
Parameters {'rf__n_estimators': 196, 'rf__max_depth': 12, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='sqrt', n_estimators=196,
random_state=100))])
cv score: [0.61781609 0.65804598 0.61350575 0.71834625 0.40697674]
----------------------------------------
Trial 4899
----------------------------------------
Parameters {'xgb__n_estimators': 48, 'xgb__subsample': 1.0, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.003, 'xgb__max_depth': 13, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=13,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=48,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.61063218 0.63649425 0.65229885 0.49612403 0.43927649]
----------------------------------------
Trial 4900
----------------------------------------
Parameters {'gb__n_estimators': 118, 'gb__subsample': 0.7, 'gb__learning_rate': 0.07, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=12,
max_features='log2',
n_estimators=118, random_state=100,
subsample=0.7))])
cv score: [0.60488506 0.67528736 0.58045977 0.69121447 0.4625323 ]
----------------------------------------
Trial 4901
----------------------------------------
Parameters {'rf__n_estimators': 33, 'rf__max_depth': 12, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=12,
max_features='log2', n_estimators=33,
random_state=100))])
cv score: [0.62068966 0.65948276 0.6329023 0.64728682 0.33074935]
----------------------------------------
Trial 4902
----------------------------------------
Parameters {'rf__n_estimators': 124, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='sqrt', n_estimators=124,
random_state=100))])
cv score: [0.77729885 0.63505747 0.67025862 0.63565891 0.39147287]
----------------------------------------
Trial 4903
----------------------------------------
Parameters {'xgb__n_estimators': 67, 'xgb__subsample': 0.65, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=67,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.65, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77729885 0.65517241 0.69683908 0.64018088 0.44767442]
----------------------------------------
Trial 4904
----------------------------------------
Parameters {'rf__n_estimators': 67, 'rf__max_depth': 14, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=14, max_features='log2',
n_estimators=67, random_state=100))])
cv score: [0.5545977 0.62859195 0.5783046 0.59173127 0.45994832]
----------------------------------------
Trial 4905
----------------------------------------
Parameters {'gb__n_estimators': 92, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='log2',
n_estimators=92, random_state=100,
subsample=0.85))])
cv score: [0.60344828 0.51867816 0.55316092 0.68087855 0.38242894]
----------------------------------------
Trial 4906
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 6, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=6,
max_features='sqrt', n_estimators=118,
random_state=100))])
cv score: [0.70689655 0.64798851 0.65948276 0.65374677 0.41472868]
----------------------------------------
Trial 4907
----------------------------------------
Parameters {'gb__n_estimators': 122, 'gb__subsample': 0.65, 'gb__learning_rate': 0.001, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=13,
n_estimators=122, random_state=100,
subsample=0.65))])
cv score: [0.56609195 0.72270115 0.63362069 0.66925065 0.48837209]
----------------------------------------
Trial 4908
----------------------------------------
Parameters {'xgb__n_estimators': 62, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=62,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.75143678 0.6795977 0.71264368 0.57364341 0.52067183]
----------------------------------------
Trial 4909
----------------------------------------
Parameters {'xgb__n_estimators': 42, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.07, 'xgb__max_depth': 12, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=12,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=42,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.68103448 0.59770115 0.57622739 0.45994832]
----------------------------------------
Trial 4910
----------------------------------------
Parameters {'xgb__n_estimators': 15, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.8, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.8, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=15,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.71408046 0.72413793 0.61781609 0.62790698 0.4754522 ]
----------------------------------------
Trial 4911
----------------------------------------
Parameters {'rf__n_estimators': 32, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=32, random_state=100))])
cv score: [0.59195402 0.70833333 0.60775862 0.58010336 0.50129199]
----------------------------------------
Trial 4912
----------------------------------------
Parameters {'xgb__n_estimators': 133, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=133,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.69252874 0.72126437 0.63178295 0.39018088]
----------------------------------------
Trial 4913
----------------------------------------
Parameters {'xgb__n_estimators': 162, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 5, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=5,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=162,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.70258621 0.65086207 0.65633075 0.42894057]
----------------------------------------
Trial 4914
----------------------------------------
Parameters {'gb__n_estimators': 192, 'gb__subsample': 0.7, 'gb__learning_rate': 0.1, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=14, max_features='sqrt',
n_estimators=192, random_state=100,
subsample=0.7))])
cv score: [0.64655172 0.6091954 0.58908046 0.65503876 0.47416021]
----------------------------------------
Trial 4915
----------------------------------------
Parameters {'rf__n_estimators': 166, 'rf__max_depth': 7, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features='sqrt', n_estimators=166,
random_state=100))])
cv score: [0.65373563 0.63936782 0.61781609 0.65245478 0.45090439]
----------------------------------------
Trial 4916
----------------------------------------
Parameters {'xgb__n_estimators': 86, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=86,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.60775862 0.57327586 0.68346253 0.43281654]
----------------------------------------
Trial 4917
----------------------------------------
Parameters {'rf__n_estimators': 14, 'rf__max_depth': 13, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='sqrt', n_estimators=14,
random_state=100))])
cv score: [0.62212644 0.63649425 0.59985632 0.49806202 0.43604651]
----------------------------------------
Trial 4918
----------------------------------------
Parameters {'gb__n_estimators': 73, 'gb__subsample': 0.85, 'gb__learning_rate': 0.003, 'gb__max_depth': 11, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=11,
n_estimators=73, random_state=100,
subsample=0.85))])
cv score: [0.59051724 0.73132184 0.60344828 0.69121447 0.35400517]
----------------------------------------
Trial 4919
----------------------------------------
Parameters {'gb__n_estimators': 124, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
n_estimators=124, random_state=100,
subsample=0.85))])
cv score: [0.5545977 0.61925287 0.57758621 0.53875969 0.4250646 ]
----------------------------------------
Trial 4920
----------------------------------------
Parameters {'rf__n_estimators': 151, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=151,
random_state=100))])
cv score: [0.61422414 0.60057471 0.5466954 0.57041344 0.45930233]
----------------------------------------
Trial 4921
----------------------------------------
Parameters {'rf__n_estimators': 30, 'rf__max_depth': 7, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=7,
max_features=None, n_estimators=30,
random_state=100))])
cv score: [0.56321839 0.6091954 0.48060345 0.62273902 0.42958656]
----------------------------------------
Trial 4922
----------------------------------------
Parameters {'rf__n_estimators': 71, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=10, max_features='log2',
n_estimators=71, random_state=100))])
cv score: [0.64511494 0.63793103 0.48994253 0.67054264 0.39664083]
----------------------------------------
Trial 4923
----------------------------------------
Parameters {'rf__n_estimators': 160, 'rf__max_depth': 3, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=3,
max_features='log2', n_estimators=160,
random_state=100))])
cv score: [0.77298851 0.6408046 0.68821839 0.64082687 0.39793282]
----------------------------------------
Trial 4924
----------------------------------------
Parameters {'rf__n_estimators': 18, 'rf__max_depth': 2, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features='sqrt',
n_estimators=18, random_state=100))])
cv score: [0.67097701 0.69396552 0.56609195 0.52390181 0.36950904]
----------------------------------------
Trial 4925
----------------------------------------
Parameters {'xgb__n_estimators': 24, 'xgb__subsample': 0.7, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=24,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.7, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56034483 0.72988506 0.58908046 0.58527132 0.46640827]
----------------------------------------
Trial 4926
----------------------------------------
Parameters {'gb__n_estimators': 58, 'gb__subsample': 0.65, 'gb__learning_rate': 0.1, 'gb__max_depth': 13, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=13, max_features='sqrt',
n_estimators=58, random_state=100,
subsample=0.65))])
cv score: [0.55747126 0.66522989 0.50431034 0.63049096 0.49354005]
----------------------------------------
Trial 4927
----------------------------------------
Parameters {'gb__n_estimators': 117, 'gb__subsample': 0.75, 'gb__learning_rate': 0.003, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=9,
n_estimators=117, random_state=100,
subsample=0.75))])
cv score: [0.55028736 0.7183908 0.5387931 0.67700258 0.4379845 ]
----------------------------------------
Trial 4928
----------------------------------------
Parameters {'gb__n_estimators': 10, 'gb__subsample': 1.0, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
max_features='sqrt',
n_estimators=10,
random_state=100))])
cv score: [0.65948276 0.68965517 0.63649425 0.56718346 0.4754522 ]
----------------------------------------
Trial 4929
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 1.0, 'gb__learning_rate': 0.07, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=9,
n_estimators=115,
random_state=100))])
cv score: [0.5545977 0.57399425 0.47988506 0.68863049 0.47997416]
----------------------------------------
Trial 4930
----------------------------------------
Parameters {'gb__n_estimators': 160, 'gb__subsample': 0.8, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=160, random_state=100,
subsample=0.8))])
cv score: [0.5933908 0.6566092 0.53448276 0.69121447 0.36821705]
----------------------------------------
Trial 4931
----------------------------------------
Parameters {'gb__n_estimators': 11, 'gb__subsample': 0.9, 'gb__learning_rate': 0.03, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=8,
n_estimators=11, random_state=100,
subsample=0.9))])
cv score: [0.55316092 0.74856322 0.5 0.59689922 0.42958656]
----------------------------------------
Trial 4932
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 14, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=14,
max_features='sqrt',
random_state=100))])
cv score: [0.61278736 0.68318966 0.6408046 0.7118863 0.38953488]
----------------------------------------
Trial 4933
----------------------------------------
Parameters {'rf__n_estimators': 11, 'rf__max_depth': 8, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=8,
max_features=None, n_estimators=11,
random_state=100))])
cv score: [0.60991379 0.59554598 0.5466954 0.57170543 0.45930233]
----------------------------------------
Trial 4934
----------------------------------------
Parameters {'gb__n_estimators': 83, 'gb__subsample': 0.65, 'gb__learning_rate': 0.003, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=12,
max_features='log2',
n_estimators=83, random_state=100,
subsample=0.65))])
cv score: [0.61206897 0.66522989 0.57183908 0.67571059 0.44056848]
----------------------------------------
Trial 4935
----------------------------------------
Parameters {'gb__n_estimators': 164, 'gb__subsample': 1.0, 'gb__learning_rate': 0.01, 'gb__max_depth': 7, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=7,
max_features='log2',
n_estimators=164,
random_state=100))])
cv score: [0.62212644 0.67816092 0.62643678 0.63307494 0.4379845 ]
----------------------------------------
Trial 4936
----------------------------------------
Parameters {'gb__n_estimators': 143, 'gb__subsample': 0.7, 'gb__learning_rate': 0.7, 'gb__max_depth': 7, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=7,
max_features='sqrt',
n_estimators=143, random_state=100,
subsample=0.7))])
cv score: [0.54741379 0.58908046 0.6558908 0.59366925 0.40374677]
----------------------------------------
Trial 4937
----------------------------------------
Parameters {'gb__n_estimators': 94, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=4,
max_features='sqrt',
n_estimators=94, random_state=100,
subsample=0.75))])
cv score: [0.53735632 0.70977011 0.52873563 0.59689922 0.5245478 ]
----------------------------------------
Trial 4938
----------------------------------------
Parameters {'rf__n_estimators': 161, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=161, random_state=100))])
cv score: [0.55747126 0.64942529 0.57471264 0.65633075 0.43410853]
----------------------------------------
Trial 4939
----------------------------------------
Parameters {'xgb__n_estimators': 29, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.1, 'xgb__max_depth': 2, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=2,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=29,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.64511494 0.70689655 0.52586207 0.59302326 0.44444444]
----------------------------------------
Trial 4940
----------------------------------------
Parameters {'gb__n_estimators': 54, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=7,
n_estimators=54, random_state=100,
subsample=0.9))])
cv score: [0.59051724 0.73994253 0.62643678 0.67571059 0.40956072]
----------------------------------------
Trial 4941
----------------------------------------
Parameters {'xgb__n_estimators': 185, 'xgb__subsample': 0.75, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 1.0, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1.0, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=185,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55316092 0.69827586 0.54885057 0.53617571 0.47157623]
----------------------------------------
Trial 4942
----------------------------------------
Parameters {'gb__n_estimators': 145, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 10, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=10,
n_estimators=145, random_state=100,
subsample=0.65))])
cv score: [0.47988506 0.64224138 0.56752874 0.67054264 0.55555556]
----------------------------------------
Trial 4943
----------------------------------------
Parameters {'rf__n_estimators': 170, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features=None, n_estimators=170,
random_state=100))])
cv score: [0.66307471 0.64295977 0.48275862 0.56718346 0.41860465]
----------------------------------------
Trial 4944
----------------------------------------
Parameters {'gb__n_estimators': 39, 'gb__subsample': 1.0, 'gb__learning_rate': 0.03, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.03, max_depth=10,
max_features='log2',
n_estimators=39,
random_state=100))])
cv score: [0.55028736 0.67672414 0.52873563 0.70284238 0.50258398]
----------------------------------------
Trial 4945
----------------------------------------
Parameters {'rf__n_estimators': 197, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=197,
random_state=100))])
cv score: [0.57327586 0.59554598 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 4946
----------------------------------------
Parameters {'xgb__n_estimators': 52, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.07, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=52,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.58333333 0.66666667 0.48706897 0.55943152 0.4870801 ]
----------------------------------------
Trial 4947
----------------------------------------
Parameters {'xgb__n_estimators': 94, 'xgb__subsample': 0.95, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.01, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=94,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.55316092 0.6091954 0.59626437 0.52325581 0.39793282]
----------------------------------------
Trial 4948
----------------------------------------
Parameters {'xgb__n_estimators': 119, 'xgb__subsample': 0.8, 'xgb__learning_rate': 1.0, 'xgb__gamma': 0.07, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=1.0,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=119,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63505747 0.75718391 0.58189655 0.68992248 0.34108527]
----------------------------------------
Trial 4949
----------------------------------------
Parameters {'gb__n_estimators': 19, 'gb__subsample': 0.9, 'gb__learning_rate': 0.7, 'gb__max_depth': 8, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=8,
n_estimators=19, random_state=100,
subsample=0.9))])
cv score: [0.66810345 0.67241379 0.49425287 0.61111111 0.38501292]
----------------------------------------
Trial 4950
----------------------------------------
Parameters {'rf__n_estimators': 68, 'rf__max_depth': 2, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=2, max_features=None,
n_estimators=68, random_state=100))])
cv score: [0.75431034 0.62571839 0.5237069 0.59302326 0.3875969 ]
----------------------------------------
Trial 4951
----------------------------------------
Parameters {'rf__n_estimators': 21, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=21, random_state=100))])
cv score: [0.55531609 0.68678161 0.4295977 0.58656331 0.5749354 ]
----------------------------------------
Trial 4952
----------------------------------------
Parameters {'rf__n_estimators': 29, 'rf__max_depth': 4, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=4,
max_features='log2', n_estimators=29,
random_state=100))])
cv score: [0.74712644 0.63649425 0.60775862 0.7118863 0.44186047]
----------------------------------------
Trial 4953
----------------------------------------
Parameters {'gb__n_estimators': 131, 'gb__subsample': 0.75, 'gb__learning_rate': 0.3, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=11,
max_features='log2',
n_estimators=131, random_state=100,
subsample=0.75))])
cv score: [0.66522989 0.62356322 0.55028736 0.65891473 0.4625323 ]
----------------------------------------
Trial 4954
----------------------------------------
Parameters {'xgb__n_estimators': 147, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.3, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.3, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=147,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.71551724 0.62212644 0.5749354 0.45090439]
----------------------------------------
Trial 4955
----------------------------------------
Parameters {'rf__n_estimators': 152, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=11,
max_features='sqrt', n_estimators=152,
random_state=100))])
cv score: [0.5862069 0.69252874 0.57902299 0.68346253 0.41989664]
----------------------------------------
Trial 4956
----------------------------------------
Parameters {'rf__n_estimators': 39, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=13, max_features='log2',
n_estimators=39, random_state=100))])
cv score: [0.55962644 0.7033046 0.63721264 0.59883721 0.46317829]
----------------------------------------
Trial 4957
----------------------------------------
Parameters {'rf__n_estimators': 23, 'rf__max_depth': 13, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=13,
max_features='log2', n_estimators=23,
random_state=100))])
cv score: [0.60991379 0.66235632 0.6091954 0.63307494 0.41537468]
----------------------------------------
Trial 4958
----------------------------------------
Parameters {'gb__n_estimators': 191, 'gb__subsample': 0.9, 'gb__learning_rate': 0.001, 'gb__max_depth': 14, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=14,
max_features='log2',
n_estimators=191, random_state=100,
subsample=0.9))])
cv score: [0.59195402 0.70689655 0.61063218 0.65633075 0.46124031]
----------------------------------------
Trial 4959
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 3, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=3, max_features='sqrt',
n_estimators=191, random_state=100))])
cv score: [0.7341954 0.62643678 0.64942529 0.62273902 0.41731266]
----------------------------------------
Trial 4960
----------------------------------------
Parameters {'xgb__n_estimators': 120, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.001, 'xgb__gamma': 0.03, 'xgb__max_depth': 9, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.001,
max_delta_step=0, max_depth=9,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=120,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77586207 0.56034483 0.68462644 0.55232558 0.41925065]
----------------------------------------
Trial 4961
----------------------------------------
Parameters {'gb__n_estimators': 180, 'gb__subsample': 1.0, 'gb__learning_rate': 1.0, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=1.0, max_depth=12,
max_features='log2',
n_estimators=180,
random_state=100))])
cv score: [0.51724138 0.70114943 0.45833333 0.65633075 0.42764858]
----------------------------------------
Trial 4962
----------------------------------------
Parameters {'xgb__n_estimators': 193, 'xgb__subsample': 0.95, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.001, 'xgb__max_depth': 4, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=4,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=193,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.95, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.56752874 0.72701149 0.60057471 0.54005168 0.37338501]
----------------------------------------
Trial 4963
----------------------------------------
Parameters {'xgb__n_estimators': 195, 'xgb__subsample': 1.0, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.03, 'xgb__max_depth': 10, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.03, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=10,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=195,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=1.0, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.6091954 0.72988506 0.73706897 0.64082687 0.37726098]
----------------------------------------
Trial 4964
----------------------------------------
Parameters {'gb__n_estimators': 34, 'gb__subsample': 0.8, 'gb__learning_rate': 0.07, 'gb__max_depth': 3, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, n_estimators=34,
random_state=100, subsample=0.8))])
cv score: [0.65804598 0.70689655 0.70402299 0.6627907 0.43152455]
----------------------------------------
Trial 4965
----------------------------------------
Parameters {'rf__n_estimators': 26, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=26, random_state=100))])
cv score: [0.56537356 0.61278736 0.5079023 0.53229974 0.48126615]
----------------------------------------
Trial 4966
----------------------------------------
Parameters {'gb__n_estimators': 115, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 7, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=7,
n_estimators=115, random_state=100,
subsample=0.95))])
cv score: [0.61350575 0.71408046 0.5704023 0.67958656 0.39147287]
----------------------------------------
Trial 4967
----------------------------------------
Parameters {'gb__n_estimators': 149, 'gb__subsample': 0.95, 'gb__learning_rate': 0.01, 'gb__max_depth': 3, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01,
max_features='log2',
n_estimators=149, random_state=100,
subsample=0.95))])
cv score: [0.73132184 0.64942529 0.70545977 0.66020672 0.38630491]
----------------------------------------
Trial 4968
----------------------------------------
Parameters {'rf__n_estimators': 35, 'rf__max_depth': 5, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features=None,
n_estimators=35, random_state=100))])
cv score: [0.75 0.68103448 0.6091954 0.61757106 0.5 ]
----------------------------------------
Trial 4969
----------------------------------------
Parameters {'gb__n_estimators': 126, 'gb__subsample': 0.9, 'gb__learning_rate': 0.003, 'gb__max_depth': 8, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.003, max_depth=8,
max_features='sqrt',
n_estimators=126, random_state=100,
subsample=0.9))])
cv score: [0.65086207 0.68965517 0.60057471 0.65374677 0.44315245]
----------------------------------------
Trial 4970
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 0.85, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
n_estimators=147, random_state=100,
subsample=0.85))])
cv score: [0.64798851 0.71551724 0.55316092 0.47932817 0.45994832]
----------------------------------------
Trial 4971
----------------------------------------
Parameters {'gb__n_estimators': 179, 'gb__subsample': 0.85, 'gb__learning_rate': 0.3, 'gb__max_depth': 12, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.3, max_depth=12,
max_features='log2',
n_estimators=179, random_state=100,
subsample=0.85))])
cv score: [0.59195402 0.72701149 0.51724138 0.60077519 0.38630491]
----------------------------------------
Trial 4972
----------------------------------------
Parameters {'rf__n_estimators': 97, 'rf__max_depth': 11, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=11, max_features='sqrt',
n_estimators=97, random_state=100))])
cv score: [0.60201149 0.65804598 0.57902299 0.62144703 0.44832041]
----------------------------------------
Trial 4973
----------------------------------------
Parameters {'xgb__n_estimators': 73, 'xgb__subsample': 0.75, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.1, 'xgb__max_depth': 3, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=73,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.75, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74137931 0.64655172 0.65301724 0.64534884 0.45219638]
----------------------------------------
Trial 4974
----------------------------------------
Parameters {'xgb__n_estimators': 71, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.7, 'xgb__gamma': 0.01, 'xgb__max_depth': 8, 'xgb__colsample_bytree': 0.6, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.6, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.7,
max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=71,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.62212644 0.71551724 0.48994253 0.55297158 0.43281654]
----------------------------------------
Trial 4975
----------------------------------------
Parameters {'gb__n_estimators': 111, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 11, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=11,
max_features='log2',
n_estimators=111, random_state=100,
subsample=0.95))])
cv score: [0.59770115 0.70545977 0.55316092 0.73514212 0.41472868]
----------------------------------------
Trial 4976
----------------------------------------
Parameters {'gb__n_estimators': 147, 'gb__subsample': 1.0, 'gb__learning_rate': 0.1, 'gb__max_depth': 12, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=12, n_estimators=147,
random_state=100))])
cv score: [0.67887931 0.5941092 0.60775862 0.54328165 0.29780362]
----------------------------------------
Trial 4977
----------------------------------------
Parameters {'gb__n_estimators': 70, 'gb__subsample': 0.8, 'gb__learning_rate': 0.01, 'gb__max_depth': 4, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.01, max_depth=4,
n_estimators=70, random_state=100,
subsample=0.8))])
cv score: [0.67528736 0.67385057 0.63936782 0.65245478 0.42248062]
----------------------------------------
Trial 4978
----------------------------------------
Parameters {'xgb__n_estimators': 74, 'xgb__subsample': 0.9, 'xgb__learning_rate': 0.3, 'xgb__gamma': 0.01, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.3,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=74,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.9, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.59482759 0.73563218 0.65086207 0.60465116 0.43540052]
----------------------------------------
Trial 4979
----------------------------------------
Parameters {'gb__n_estimators': 157, 'gb__subsample': 0.95, 'gb__learning_rate': 0.001, 'gb__max_depth': 9, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=9,
n_estimators=157, random_state=100,
subsample=0.95))])
cv score: [0.55028736 0.6954023 0.59482759 0.63307494 0.38630491]
----------------------------------------
Trial 4980
----------------------------------------
Parameters {'rf__n_estimators': 100, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
random_state=100))])
cv score: [0.75718391 0.65517241 0.64367816 0.65762274 0.44056848]
----------------------------------------
Trial 4981
----------------------------------------
Parameters {'rf__n_estimators': 114, 'rf__max_depth': 4, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=4, max_features=None,
n_estimators=114, random_state=100))])
cv score: [0.75 0.64798851 0.6408046 0.64470284 0.43023256]
----------------------------------------
Trial 4982
----------------------------------------
Parameters {'rf__n_estimators': 86, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=86,
random_state=100))])
cv score: [0.57183908 0.59770115 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 4983
----------------------------------------
Parameters {'gb__n_estimators': 77, 'gb__subsample': 0.65, 'gb__learning_rate': 0.07, 'gb__max_depth': 13, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.07, max_depth=13,
n_estimators=77, random_state=100,
subsample=0.65))])
cv score: [0.58333333 0.71551724 0.59051724 0.70542636 0.43927649]
----------------------------------------
Trial 4984
----------------------------------------
Parameters {'rf__n_estimators': 149, 'rf__max_depth': 9, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=9,
max_features='sqrt', n_estimators=149,
random_state=100))])
cv score: [0.62643678 0.66666667 0.60057471 0.68217054 0.4754522 ]
----------------------------------------
Trial 4985
----------------------------------------
Parameters {'rf__n_estimators': 118, 'rf__max_depth': 5, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=5, max_features='sqrt',
n_estimators=118, random_state=100))])
cv score: [0.74712644 0.63649425 0.6637931 0.58010336 0.39405685]
----------------------------------------
Trial 4986
----------------------------------------
Parameters {'rf__n_estimators': 75, 'rf__max_depth': 5, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=5,
max_features='log2', n_estimators=75,
random_state=100))])
cv score: [0.71408046 0.64367816 0.65373563 0.5994832 0.37855297]
----------------------------------------
Trial 4987
----------------------------------------
Parameters {'rf__n_estimators': 136, 'rf__max_depth': 10, 'rf__max_features': 'sqrt', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='sqrt', n_estimators=136,
random_state=100))])
cv score: [0.5933908 0.67385057 0.64655172 0.66149871 0.43152455]
----------------------------------------
Trial 4988
----------------------------------------
Parameters {'gb__n_estimators': 49, 'gb__subsample': 0.85, 'gb__learning_rate': 0.001, 'gb__max_depth': 4, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.001, max_depth=4,
max_features='sqrt',
n_estimators=49, random_state=100,
subsample=0.85))])
cv score: [0.72270115 0.6091954 0.67528736 0.67571059 0.43023256]
----------------------------------------
Trial 4989
----------------------------------------
Parameters {'xgb__n_estimators': 21, 'xgb__subsample': 0.8, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.003, 'xgb__max_depth': 6, 'xgb__colsample_bytree': 0.7, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.7, gamma=0.003, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=21,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.8, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.74928161 0.58908046 0.69755747 0.58591731 0.42635659]
----------------------------------------
Trial 4990
----------------------------------------
Parameters {'rf__n_estimators': 191, 'rf__max_depth': 6, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': True}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(max_depth=6, max_features='log2',
n_estimators=191, random_state=100))])
cv score: [0.65948276 0.63362069 0.60201149 0.60981912 0.40697674]
----------------------------------------
Trial 4991
----------------------------------------
Parameters {'gb__n_estimators': 156, 'gb__subsample': 0.8, 'gb__learning_rate': 0.1, 'gb__max_depth': 10, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(max_depth=10, max_features='log2',
n_estimators=156, random_state=100,
subsample=0.8))])
cv score: [0.64511494 0.63649425 0.51436782 0.61886305 0.4870801 ]
----------------------------------------
Trial 4992
----------------------------------------
Parameters {'xgb__n_estimators': 140, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.1, 'xgb__gamma': 0.07, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.07, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.1,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=140,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.60775862 0.71982759 0.60057471 0.62403101 0.41602067]
----------------------------------------
Trial 4993
----------------------------------------
Parameters {'gb__n_estimators': 18, 'gb__subsample': 1.0, 'gb__learning_rate': 0.7, 'gb__max_depth': 2, 'gb__max_features': None, 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=2,
n_estimators=18,
random_state=100))])
cv score: [0.7341954 0.54454023 0.67887931 0.73191214 0.44379845]
----------------------------------------
Trial 4994
----------------------------------------
Parameters {'rf__n_estimators': 189, 'rf__max_depth': 10, 'rf__max_features': 'log2', 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features='log2', n_estimators=189,
random_state=100))])
cv score: [0.5862069 0.66954023 0.62212644 0.68346253 0.41860465]
----------------------------------------
Trial 4995
----------------------------------------
Parameters {'xgb__n_estimators': 172, 'xgb__subsample': 0.85, 'xgb__learning_rate': 0.01, 'xgb__gamma': 0.001, 'xgb__max_depth': 14, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.001, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.01,
max_delta_step=0, max_depth=14,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=172,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.85, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.63936782 0.70114943 0.67672414 0.6627907 0.41602067]
----------------------------------------
Trial 4996
----------------------------------------
Parameters {'rf__n_estimators': 98, 'rf__max_depth': 10, 'rf__max_features': None, 'rf__random_state': 100, 'rf__bootstrap': False}
Pipeline: Pipeline(steps=[('rf',
RandomForestClassifier(bootstrap=False, max_depth=10,
max_features=None, n_estimators=98,
random_state=100))])
cv score: [0.57183908 0.59554598 0.56537356 0.56912145 0.46640827]
----------------------------------------
Trial 4997
----------------------------------------
Parameters {'xgb__n_estimators': 137, 'xgb__subsample': 0.6, 'xgb__learning_rate': 0.003, 'xgb__gamma': 0.01, 'xgb__max_depth': 11, 'xgb__colsample_bytree': 0.9, 'xgb__random_state': 100}
Pipeline: Pipeline(steps=[('xgb',
XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=0.9, gamma=0.01, gpu_id=-1,
importance_type='gain',
interaction_constraints='', learning_rate=0.003,
max_delta_step=0, max_depth=11,
min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=137,
n_jobs=16, num_parallel_tree=1, random_state=100,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
subsample=0.6, tree_method='exact',
validate_parameters=1, verbosity=None))])
cv score: [0.77873563 0.68390805 0.67097701 0.59883721 0.44573643]
----------------------------------------
Trial 4998
----------------------------------------
Parameters {'gb__n_estimators': 38, 'gb__subsample': 0.8, 'gb__learning_rate': 0.7, 'gb__max_depth': 6, 'gb__max_features': 'log2', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=6,
max_features='log2',
n_estimators=38, random_state=100,
subsample=0.8))])
cv score: [0.45689655 0.33764368 0.68247126 0.65374677 0.54651163]
----------------------------------------
Trial 4999
----------------------------------------
Parameters {'gb__n_estimators': 150, 'gb__subsample': 0.65, 'gb__learning_rate': 0.7, 'gb__max_depth': 14, 'gb__max_features': 'sqrt', 'gb__random_state': 100}
Pipeline: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=150, random_state=100,
subsample=0.65))])
cv score: [0.51724138 0.66954023 0.67816092 0.77777778 0.33850129]
----------------------------------------
Selecting and refitting best classifier
----------------------------------------
best score: 0.673700213846565
best pipe: Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=150, random_state=100,
subsample=0.65))])
In [25]:
# best_estimator = XGBClassifier(base_score=0.5, booster='gbtree',
# colsample_bylevel=1, colsample_bynode=1,
# colsample_bytree=0.7, gamma=0.1, gpu_id=-1,
# importance_type='gain',
# interaction_constraints='', learning_rate=0.03,
# max_delta_step=0, max_depth=5,
# min_child_weight=1,
# monotone_constraints='()', n_estimators=151,
# n_jobs=16, num_parallel_tree=1, random_state=100,
# reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
# subsample=0.7, tree_method='exact',
# validate_parameters=1, verbosity=None)
In [26]:
best_estimator.fit(X_rest, y_rest)
Out[26]:
Pipeline(steps=[('gb',
GradientBoostingClassifier(learning_rate=0.7, max_depth=14,
max_features='sqrt',
n_estimators=150, random_state=100,
subsample=0.65))])
In [27]:
mmh.get_test_scores(X_rest, y_rest, X_test, y_test, best_estimator)
Performance on the test set Classification accuracy: 0.8991596638655462 AUROC: 0.4908256880733945 Recall: 0.0 F1 Score: 0.0 Log-loss: 3.4829152599478035
In [28]:
cm_test = confusion_matrix(y_test, best_estimator.predict(X_test), normalize='true')
mmh.plot_confusion_matrix(cm_test, [0, 1])
In [ ]: